{"id":1827,"date":"2026-08-21T11:43:59","date_gmt":"2026-08-21T11:43:59","guid":{"rendered":"https:\/\/predictleads.com\/blog\/company-intelligence-api-comparison\/"},"modified":"2026-10-21T15:01:32","modified_gmt":"2026-10-21T15:01:32","slug":"company-intelligence-api-comparison","status":"publish","type":"post","link":"https:\/\/predictleads.com\/blog\/company-intelligence-api-comparison\/","title":{"rendered":"Best Company Data APIs for AI Agents (October 2026)"},"content":{"rendered":"\n<p><em>By Robert Fon \/ October 21, 2026<\/em><\/p>\n\n<p>The best company data APIs for AI agents in October 2026 return structured, timestamped, source-linked company signals that an agent can filter, cite, and act on, not raw pages or static profiles. Most teams still wire their agents to a contact database or a web crawl, then spend weeks cleaning up confident answers with no source behind them. PredictLeads ranks first in this company intelligence API comparison because it delivers Job Openings, Technology Detections, News Events, Financing Events, and Connections through one API and a native Model Context Protocol (MCP) server. Below are the six criteria we scored, the seven providers worth evaluating, and a side-by-side table.<\/p>\n\n<p><strong>TLDR:<\/strong><\/p>\n<ul>\n<li>An AI agent reasons only as well as the record it retrieves, so a company data API for agents should return categorized, timestamped records with a source URL.<\/li>\n<li>MCP access is no longer rare: Coresignal, Crustdata, Explorium, and PredictLeads all offer hosted MCP servers, so the real difference is the data behind the server.<\/li>\n<li>Most providers here center on people records, firmographics, or one signal type; none of the others document categorized news events, source-linked technology detections, and company relationships in a single API.<\/li>\n<li>Score providers on signal breadth, structure, source transparency, historical timestamps, agent and delivery access, and self-serve access.<\/li>\n<li>PredictLeads covers 120M+ companies, with 2B+ Technology Detections and 279.2M+ Job Openings since 2018 and 371.8M+ Connections since 2019, delivered by API, flat files, webhooks, and MCP.<\/li>\n<\/ul>\n\n<h2>What Is a Company Data API for AI Agents?<\/h2>\n\n<p>A company data API for AI agents is a programmatic source of structured, company-level records that an agent calls as a tool, often through a <a href=\"https:\/\/modelcontextprotocol.io\/\" target=\"_blank\" rel=\"noopener\">Model Context Protocol<\/a> server, and uses as evidence. The difference from a classic enrichment API is the consumer: a rep can ignore a stale field, but an agent repeats it in an email, a score, or a research memo. That puts three demands on the data: a predictable schema, a timestamp on every fact, and a source the agent can cite.<\/p>\n\n<svg viewBox=\"0 0 1200 470\" role=\"img\" aria-label=\"Anatomy of an agent-ready company record: a simplified PredictLeads News Events record with a category, a found_at timestamp, a normalized amount, a confidence score, a source URL, and a company key, annotated as categorized, timestamped, source-linked, and joinable.\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:100%;height:auto;display:block;margin:24px 0;\">\n  <title>Anatomy of an agent-ready company record<\/title>\n  <rect width=\"1200\" height=\"470\" rx=\"18\" fill=\"#f3ecf8\"\/>\n  <text x=\"600\" y=\"48\" text-anchor=\"middle\" font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\" font-size=\"25\" font-weight=\"800\" font-style=\"italic\" fill=\"#76508e\">Anatomy of an agent-ready company record<\/text>\n  <text x=\"600\" y=\"78\" text-anchor=\"middle\" font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\" font-size=\"14\" fill=\"#5b3d70\">Simplified PredictLeads News Events record. Field names are real; values are placeholders.<\/text>\n  <rect x=\"40\" y=\"105\" width=\"560\" height=\"330\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n  <g font-family=\"Consolas,'Courier New',monospace\" font-size=\"15\" fill=\"#3a2f4d\">\n    <text x=\"66\" y=\"145\">{<\/text>\n    <text x=\"86\" y=\"185\"><tspan fill=\"#76508e\" font-weight=\"700\">&#8220;category&#8221;<\/tspan>: &#8220;receives_financing&#8221;,<\/text>\n    <text x=\"86\" y=\"225\"><tspan fill=\"#76508e\" font-weight=\"700\">&#8220;found_at&#8221;<\/tspan>: &#8220;&lt;ISO 8601 timestamp&gt;&#8221;,<\/text>\n    <text x=\"86\" y=\"265\"><tspan fill=\"#76508e\" font-weight=\"700\">&#8220;amount_normalized&#8221;<\/tspan>: &lt;USD integer&gt;,<\/text>\n    <text x=\"86\" y=\"305\"><tspan fill=\"#76508e\" font-weight=\"700\">&#8220;confidence&#8221;<\/tspan>: &lt;0 to 1&gt;,<\/text>\n    <text x=\"86\" y=\"345\"><tspan fill=\"#76508e\" font-weight=\"700\">&#8220;most_relevant_source&#8221;<\/tspan>: { &#8220;url&#8221;: &#8220;&lt;article URL&gt;&#8221; },<\/text>\n    <text x=\"86\" y=\"385\"><tspan fill=\"#76508e\" font-weight=\"700\">&#8220;company1&#8221;<\/tspan>: &#8220;&lt;company domain&gt;&#8221;<\/text>\n    <text x=\"66\" y=\"420\">}<\/text>\n  <\/g>\n  <g font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\" font-size=\"14\" font-weight=\"700\">\n    <circle cx=\"572\" cy=\"180\" r=\"14\" fill=\"#76508e\"\/><text x=\"572\" y=\"185\" text-anchor=\"middle\" fill=\"#ffffff\">1<\/text>\n    <circle cx=\"572\" cy=\"220\" r=\"14\" fill=\"#76508e\"\/><text x=\"572\" y=\"225\" text-anchor=\"middle\" fill=\"#ffffff\">2<\/text>\n    <circle cx=\"572\" cy=\"340\" r=\"14\" fill=\"#76508e\"\/><text x=\"572\" y=\"345\" text-anchor=\"middle\" fill=\"#ffffff\">3<\/text>\n    <circle cx=\"572\" cy=\"380\" r=\"14\" fill=\"#76508e\"\/><text x=\"572\" y=\"385\" text-anchor=\"middle\" fill=\"#ffffff\">4<\/text>\n  <\/g>\n  <g font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\">\n    <rect x=\"630\" y=\"110\" width=\"530\" height=\"74\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <circle cx=\"666\" cy=\"147\" r=\"16\" fill=\"#76508e\"\/><text x=\"666\" y=\"152\" text-anchor=\"middle\" font-size=\"15\" font-weight=\"700\" fill=\"#ffffff\">1<\/text>\n    <text x=\"696\" y=\"141\" font-size=\"17\" font-weight=\"700\" fill=\"#3a2f4d\">Categorized<\/text>\n    <text x=\"696\" y=\"165\" font-size=\"14\" fill=\"#5b3d70\">One of 37 categories: the agent filters, not parses<\/text>\n\n    <rect x=\"630\" y=\"192\" width=\"530\" height=\"74\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <circle cx=\"666\" cy=\"229\" r=\"16\" fill=\"#76508e\"\/><text x=\"666\" y=\"234\" text-anchor=\"middle\" font-size=\"15\" font-weight=\"700\" fill=\"#ffffff\">2<\/text>\n    <text x=\"696\" y=\"223\" font-size=\"17\" font-weight=\"700\" fill=\"#3a2f4d\">Timestamped<\/text>\n    <text x=\"696\" y=\"247\" font-size=\"14\" fill=\"#5b3d70\">found_at tells the agent how recent the signal is<\/text>\n\n    <rect x=\"630\" y=\"274\" width=\"530\" height=\"74\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <circle cx=\"666\" cy=\"311\" r=\"16\" fill=\"#76508e\"\/><text x=\"666\" y=\"316\" text-anchor=\"middle\" font-size=\"15\" font-weight=\"700\" fill=\"#ffffff\">3<\/text>\n    <text x=\"696\" y=\"305\" font-size=\"17\" font-weight=\"700\" fill=\"#3a2f4d\">Source-linked<\/text>\n    <text x=\"696\" y=\"329\" font-size=\"14\" fill=\"#5b3d70\">A URL the agent can cite when a rep asks why<\/text>\n\n    <rect x=\"630\" y=\"356\" width=\"530\" height=\"74\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <circle cx=\"666\" cy=\"393\" r=\"16\" fill=\"#76508e\"\/><text x=\"666\" y=\"398\" text-anchor=\"middle\" font-size=\"15\" font-weight=\"700\" fill=\"#ffffff\">4<\/text>\n    <text x=\"696\" y=\"387\" font-size=\"17\" font-weight=\"700\" fill=\"#3a2f4d\">Joinable<\/text>\n    <text x=\"696\" y=\"411\" font-size=\"14\" fill=\"#5b3d70\">A company key that joins every other dataset<\/text>\n  <\/g>\n<\/svg>\n\n<p>These are the signal types that matter most in agent workflows:<\/p>\n<ul>\n<li><strong>Firmographics:<\/strong> domain, location, industry codes, headcount, and revenue range, the anchor every other signal joins to.<\/li>\n<li><strong>Hiring signals:<\/strong> job openings by occupation and seniority, which can show where a company is investing before it announces anything.<\/li>\n<li><strong>Technographic evidence:<\/strong> <a href=\"https:\/\/predictleads.com\/blog\/technographic-data\/\">technographic data<\/a> that provides evidence of which technologies a company uses or has recently used.<\/li>\n<li><strong>Company events:<\/strong> categorized news such as launches, acquisitions, partnerships, and leadership changes.<\/li>\n<li><strong>Financing events:<\/strong> funding rounds with normalized round types and amounts.<\/li>\n<li><strong>Relationships:<\/strong> published customers, vendors, partners, and integrations.<\/li>\n<li><strong>Website changes:<\/strong> new pricing, integrations, or careers pages that mark a go-to-market shift.<\/li>\n<\/ul>\n\n<p>A company data API is not a contact database or an email finder. Our explainer on <a href=\"https:\/\/predictleads.com\/blog\/company-intelligence-api-gtm-teams\/\">what company intelligence data is and why GTM teams need it<\/a> covers that split in depth. PredictLeads covers all seven signal types for 120M+ companies across 195 countries, joined by company domain.<\/p>\n\n<h2>How We Ranked These Company Intelligence APIs<\/h2>\n\n<p>We scored every provider on six dimensions that decide whether an agent&#8217;s answer can be trusted, not how polished a dashboard looks.<\/p>\n<ol>\n<li><strong>Signal breadth:<\/strong> how many distinct company signal types an agent can query, from hiring and technographic evidence to news, funding, relationships, and website changes.<\/li>\n<li><strong>Structure and normalization:<\/strong> categorized events, normalized job titles, and occupation codes, so each tool call returns predictable fields.<\/li>\n<li><strong>Source transparency:<\/strong> whether a record carries the URL it came from, so the agent can cite its evidence.<\/li>\n<li><strong>Historical depth and timestamps:<\/strong> how far back the data goes, and whether each record shows when it was first and last seen.<\/li>\n<li><strong>Agent and delivery access:<\/strong> an MCP server for live tool calls, plus REST, bulk files, and webhooks for pipelines.<\/li>\n<li><strong>Self-serve access:<\/strong> whether a developer can get credentials and test without a sales call.<\/li>\n<\/ol>\n\n<p>Scoring comes from public documentation and neutral third-party profiles (API directories, MCP directories, and independent reviews) checked in October 2026, not from hands-on testing. Where a provider does not publish a figure or a capability, we note the gap instead of guessing at a number. Each competitor section links the third-party source we used.<\/p>\n\n<h2>Best Overall Company Data API for AI Agents: PredictLeads<\/h2>\n\n<p>PredictLeads is a B2B company intelligence and technographic data provider that turns public company activity into structured, source-backed datasets. It is the data layer under an agent, not a prospecting interface, and it is the only provider in this comparison with a documented &#8220;Yes&#8221; on all six dimensions.<\/p>\n\n<h3>What PredictLeads Offers<\/h3>\n<ul>\n<li><strong>Technology Detections:<\/strong> 2B+ detections since 2018 across 99M+ companies and 240,000+ technologies, gathered from five sources (script tags, DNS records, IP ranges, cookies, and job descriptions), with <code>behind_firewall<\/code> and <code>source_count<\/code> on each record.<\/li>\n<li><strong>Job Openings:<\/strong> 279.2M+ records since 2018 and 10.2M active at any time, classified by <a href=\"https:\/\/www.onetcenter.org\/\" target=\"_blank\" rel=\"noopener\">O*NET<\/a> occupation code and seniority and refreshed roughly every 36 hours.<\/li>\n<li><strong>News Events:<\/strong> 10M+ signals since 2016 across <a href=\"https:\/\/predictleads.com\/blog\/the-37-news-event-categories-that-signal-a-gtm-opportunity\/\">37 News Event categories<\/a>, such as <code>receives_financing<\/code> and <code>launches<\/code>.<\/li>\n<li><strong>Financing Events:<\/strong> 210,800+ rounds since 2016, normalized from <code>pre_angel<\/code> through <code>series_j<\/code>, with <code>amount_normalized<\/code> in USD.<\/li>\n<li><strong>Connections:<\/strong> 371.8M+ company relationships since 2019, each with a <code>source_url<\/code> pointing to the page where the relationship was published.<\/li>\n<li><strong>Website Evolution and Similar Companies:<\/strong> 776M+ subpages tracked since 2021, plus lookalikes for 18.9M+ companies with a text <code>reason<\/code> on the top 20 matches.<\/li>\n<\/ul>\n\n<svg viewBox=\"0 0 1200 420\" role=\"img\" aria-label=\"PredictLeads by the numbers, October 2026: 130.7 million plus companies tracked, 1.5 billion plus Technology Detections since 2018, 279.2 million plus Job Openings since 2018, 10 million plus News Events since 2016, 210,800 plus Financing Events since 2016, and 371.8 million plus Connections since 2019.\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:100%;height:auto;display:block;margin:24px 0;\">\n  <title>PredictLeads by the numbers, October 2026<\/title>\n  <rect width=\"1200\" height=\"420\" rx=\"18\" fill=\"#f3ecf8\"\/>\n  <text x=\"600\" y=\"50\" text-anchor=\"middle\" font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\" font-size=\"25\" font-weight=\"800\" font-style=\"italic\" fill=\"#76508e\">What an agent can query through one PredictLeads API<\/text>\n  <g font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\">\n    <rect x=\"50\" y=\"86\" width=\"340\" height=\"132\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <text x=\"76\" y=\"140\" font-size=\"36\" font-weight=\"800\" fill=\"#76508e\">120M+<\/text>\n    <text x=\"76\" y=\"170\" font-size=\"16\" font-weight=\"700\" fill=\"#3a2f4d\">Companies tracked<\/text>\n    <text x=\"76\" y=\"194\" font-size=\"13.5\" fill=\"#5b3d70\">195 countries, 18,265 public<\/text>\n\n    <rect x=\"430\" y=\"86\" width=\"340\" height=\"132\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <text x=\"456\" y=\"140\" font-size=\"36\" font-weight=\"800\" fill=\"#76508e\">2B+<\/text>\n    <text x=\"456\" y=\"170\" font-size=\"16\" font-weight=\"700\" fill=\"#3a2f4d\">Technology Detections<\/text>\n    <text x=\"456\" y=\"194\" font-size=\"13.5\" fill=\"#5b3d70\">since 2018, 99M+ companies<\/text>\n\n    <rect x=\"810\" y=\"86\" width=\"340\" height=\"132\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <text x=\"836\" y=\"140\" font-size=\"36\" font-weight=\"800\" fill=\"#76508e\">279.2M+<\/text>\n    <text x=\"836\" y=\"170\" font-size=\"16\" font-weight=\"700\" fill=\"#3a2f4d\">Job Openings<\/text>\n    <text x=\"836\" y=\"194\" font-size=\"13.5\" fill=\"#5b3d70\">since 2018, 10.2M active<\/text>\n\n    <rect x=\"50\" y=\"240\" width=\"340\" height=\"132\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <text x=\"76\" y=\"294\" font-size=\"36\" font-weight=\"800\" fill=\"#76508e\">10M+<\/text>\n    <text x=\"76\" y=\"324\" font-size=\"16\" font-weight=\"700\" fill=\"#3a2f4d\">News Events<\/text>\n    <text x=\"76\" y=\"348\" font-size=\"13.5\" fill=\"#5b3d70\">since 2016, 37 categories<\/text>\n\n    <rect x=\"430\" y=\"240\" width=\"340\" height=\"132\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <text x=\"456\" y=\"294\" font-size=\"36\" font-weight=\"800\" fill=\"#76508e\">210,800+<\/text>\n    <text x=\"456\" y=\"324\" font-size=\"16\" font-weight=\"700\" fill=\"#3a2f4d\">Financing Events<\/text>\n    <text x=\"456\" y=\"348\" font-size=\"13.5\" fill=\"#5b3d70\">since 2016, pre_angel to series_j<\/text>\n\n    <rect x=\"810\" y=\"240\" width=\"340\" height=\"132\" rx=\"14\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n    <text x=\"836\" y=\"294\" font-size=\"36\" font-weight=\"800\" fill=\"#76508e\">371.8M+<\/text>\n    <text x=\"836\" y=\"324\" font-size=\"16\" font-weight=\"700\" fill=\"#3a2f4d\">Connections<\/text>\n    <text x=\"836\" y=\"348\" font-size=\"13.5\" fill=\"#5b3d70\">since 2019, source_url on each<\/text>\n  <\/g>\n<\/svg>\n\n<h3>Why the Combination Matters for Agents<\/h3>\n\n<p>PredictLeads is the only provider in this comparison that natively combines categorized News Events, source-linked Technology Detections, and company-to-company Connections behind one API and one MCP server, based on the public documentation we reviewed. A single-signal source can confirm that a company raised a Series B. A multi-signal source lets the agent find companies that raised in the last 90 days, are hiring revenue operations roles, and show evidence of a competitor&#8217;s tool, then cite a source for each claim. Our guide to <a href=\"https:\/\/predictleads.com\/blog\/gtm-agent-mcp-server\/\">building a GTM agent on the PredictLeads MCP server<\/a> shows that chain end to end.<\/p>\n\n<p>Responses follow the <a href=\"https:\/\/jsonapi.org\/\" target=\"_blank\" rel=\"noopener\">JSON:API specification<\/a>, and records carry timestamps such as <code>first_seen_at<\/code>, <code>last_seen_at<\/code>, or <code>found_at<\/code>, so an agent can judge recency itself. Point any MCP client at <code>https:\/\/mcp.predictleads.com\/<\/code> with <code>X-Api-Key<\/code> and <code>X-Api-Token<\/code> headers or OAuth2, as described in the <a href=\"https:\/\/docs.predictleads.com\/mcp_integration\/introduction\" target=\"_blank\" rel=\"noopener\">MCP integration docs<\/a>. The same data ships by API, flat files, and webhooks, and new accounts get 100 free API requests before <a href=\"https:\/\/predictleads.com\/pricing\">pay-as-you-go pricing<\/a> starts.<\/p>\n\n<h2>Coresignal: Company, Employee, and Job Records<\/h2>\n\n<p>Coresignal is a public web data provider that sells company, employee, and job posting records through REST APIs.<\/p>\n\n<h3>What They Offer<\/h3>\n<ul>\n<li>Company, Employee, and Jobs data in Multi-source, Clean, and Base tiers, with Agentic Search, Company Enrichment, Collect, and Search APIs.<\/li>\n<li>A remote MCP server with OAuth sign-in and natural-language search tools.<\/li>\n<li>4.5B+ data records, including 75M+ companies, 865M+ employee profiles, and 461M+ job postings, per its <a href=\"https:\/\/apis.io\/providers\/coresignal\/\" target=\"_blank\" rel=\"noopener nofollow\">APIs.io provider profile<\/a>.<\/li>\n<li>Self-serve signup with a freemium entry point.<\/li>\n<\/ul>\n\n<p><strong>Good for:<\/strong> Agents that need deep employee and job posting coverage, such as talent research or org-chart reasoning, queried in plain English.<\/p>\n\n<p><strong>Limitation:<\/strong> The profile we reviewed does not document a categorized news event taxonomy, a company relationships dataset, or per-record source URLs, and historical start dates are not published.<\/p>\n\n<p><strong>Bottom line:<\/strong> Coresignal goes deeper on people records; PredictLeads adds 10M+ categorized News Events and 371.8M+ source-attributed Connections that an account-research agent can cite.<\/p>\n\n<p><strong>Sources:<\/strong> <a href=\"https:\/\/apis.io\/providers\/coresignal\/\" target=\"_blank\" rel=\"noopener nofollow\">APIs.io provider profile of Coresignal<\/a>; <a href=\"https:\/\/composio.dev\/toolkits\/coresignal\/framework\/claude-cowork\" target=\"_blank\" rel=\"noopener nofollow\">Composio guide to the Coresignal MCP toolkit<\/a>.<\/p>\n\n<h2>Crustdata: Real-Time People and Company Lookups<\/h2>\n\n<p>Crustdata is a Y Combinator-backed provider of real-time B2B company and people data, built for AI agent platforms, AI sales agents, and recruiting tools.<\/p>\n\n<h3>What They Offer<\/h3>\n<ul>\n<li>Company and people search and enrichment, a Web Search API that maps results to a canonical entity, and full dataset delivery.<\/li>\n<li>A hosted MCP server with 40 tools across company, people, jobs, social, and web data.<\/li>\n<li>Watchers that send webhook alerts when a tracked company or person changes.<\/li>\n<li>Reported coverage of 1B+ people and 60M+ companies, with free starter credits and per-result billing, per a <a href=\"https:\/\/pipeline.zoominfo.com\/operations\/crustdata-mcp-server\" target=\"_blank\" rel=\"noopener nofollow\">third-party review of its MCP server<\/a>.<\/li>\n<\/ul>\n\n<p><strong>Good for:<\/strong> Agent builders who need live lookups on specific people and companies, plus change alerts on a watchlist, with quick setup in Claude or Cursor.<\/p>\n\n<p><strong>Limitation:<\/strong> The review we used notes that Crustdata does not name its underlying data sources, and historical start dates per dataset are not published, which limits citation and trend analysis.<\/p>\n\n<p><strong>Bottom line:<\/strong> Crustdata is built for live retrieval on named records; PredictLeads is built for timestamped company signals with sources, including 2B+ Technology Detections since 2018 with <code>first_seen_at<\/code> and <code>last_seen_at<\/code>.<\/p>\n\n<p><strong>Sources:<\/strong> <a href=\"https:\/\/www.ycombinator.com\/companies\/crustdata\" target=\"_blank\" rel=\"noopener nofollow\">Y Combinator company profile of Crustdata<\/a>; <a href=\"https:\/\/pipeline.zoominfo.com\/operations\/crustdata-mcp-server\" target=\"_blank\" rel=\"noopener nofollow\">third-party review of the Crustdata MCP server<\/a>.<\/p>\n\n<h2>Explorium: Company and Prospect Data Through AgentSource<\/h2>\n\n<p>Explorium is a B2B data provider whose AgentSource and Vibe Prospecting products find and enrich companies and prospects for AI and GTM workflows.<\/p>\n\n<h3>What They Offer<\/h3>\n<ul>\n<li>A hosted AgentSource MCP server over streamable HTTP with API key authentication.<\/li>\n<li>Firmographic, technographic, and contact data across its business graph.<\/li>\n<li>Prospect search alongside company search, so one vendor covers accounts and people.<\/li>\n<li>Business event tracking, such as funding rounds and job changes, described in its documentation.<\/li>\n<\/ul>\n\n<p><strong>Good for:<\/strong> Teams that want company data, contact data, and agent tooling from one vendor, especially when the agent also has to find the right person to contact.<\/p>\n\n<p><strong>Limitation:<\/strong> The directory listing we reviewed does not document a company relationships dataset, per-record source URLs, or public scale figures, so coverage is hard to compare before a trial.<\/p>\n\n<p><strong>Bottom line:<\/strong> Explorium pairs accounts with contacts; PredictLeads stays company-level and adds 371.8M+ Connections since 2019, each with a <code>source_url<\/code>.<\/p>\n\n<p><strong>Sources:<\/strong> <a href=\"https:\/\/mcp.so\/servers\/explorium\" target=\"_blank\" rel=\"noopener nofollow\">MCP.so directory listing for the Explorium MCP server<\/a>.<\/p>\n\n<h2>People Data Labs: Person and Company Enrichment<\/h2>\n\n<p>People Data Labs (PDL) is a developer-focused provider of person and company records, sold as enrichment and search APIs and as bulk data licenses.<\/p>\n\n<h3>What They Offer<\/h3>\n<ul>\n<li>Person Enrichment, Person Identify, Person Search, Company Enrichment, and Company Search APIs, plus IP Enrichment and Job Posting Search.<\/li>\n<li>70M+ company profiles and person records in the billions, per a <a href=\"https:\/\/databar.ai\/blog\/article\/how-does-people-data-labs-work-a-guide-to-pdls-data-platform\" target=\"_blank\" rel=\"noopener nofollow\">Databar guide to the PDL platform<\/a> (published person counts vary by page).<\/li>\n<li>A free testing tier of up to 100 credits a month, self-serve plans, and enterprise bulk licensing with flat-file deliveries.<\/li>\n<li>Data updates that roll out monthly through the APIs.<\/li>\n<\/ul>\n\n<p><strong>Good for:<\/strong> High-volume person and company enrichment and identity resolution inside your own product, where matching a messy input to one profile is the core job.<\/p>\n\n<p><strong>Limitation:<\/strong> Records reflect the latest monthly build rather than an event stream, and the sources we reviewed do not document a first-party MCP server; MCP access is available through third-party connectors.<\/p>\n\n<p><strong>Bottom line:<\/strong> PDL fits profile enrichment; PredictLeads stores each signal as its own timestamped event with a source, such as 210,800+ Financing Events since 2016.<\/p>\n\n<p><strong>Sources:<\/strong> <a href=\"https:\/\/databar.ai\/blog\/article\/how-does-people-data-labs-work-a-guide-to-pdls-data-platform\" target=\"_blank\" rel=\"noopener nofollow\">Databar guide to the People Data Labs platform<\/a>; <a href=\"https:\/\/mcp.pipedream.com\/app\/people_data_labs\" target=\"_blank\" rel=\"noopener nofollow\">Pipedream MCP listing for People Data Labs<\/a>.<\/p>\n\n<h2>MixRank: Technographics and Mobile App Intelligence<\/h2>\n\n<p>MixRank is a B2B data provider that sells high-frequency technographic, firmographic, and people data to enterprise data teams.<\/p>\n\n<h3>What They Offer<\/h3>\n<ul>\n<li>Web technographics across 80M+ websites, refreshed as often as hourly, per its <a href=\"https:\/\/providers.apievangelist.com\/providers\/mixrank\/\" target=\"_blank\" rel=\"noopener nofollow\">API Evangelist provider profile<\/a>.<\/li>\n<li>Mobile app and software development kit (SDK) intelligence for 20M+ iOS and Android apps.<\/li>\n<li>45M+ company profiles and 800M+ people profiles.<\/li>\n<li>A JSON REST API, flat-file feeds, and native delivery to Snowflake, BigQuery, Amazon Redshift, and PostgreSQL.<\/li>\n<\/ul>\n\n<p><strong>Good for:<\/strong> Teams whose targeting depends on mobile app and SDK evidence, or that want technographic feeds landed directly in a warehouse.<\/p>\n\n<p><strong>Limitation:<\/strong> The profile we reviewed lists no MCP server and does not document a categorized news or financing event dataset, and historical depth is not published.<\/p>\n\n<p><strong>Bottom line:<\/strong> MixRank goes deeper on mobile; PredictLeads pairs 2B+ Technology Detections, including <code>behind_firewall<\/code> evidence for tools that never appear in page source, with hiring, events, funding, and relationships.<\/p>\n\n<p><strong>Sources:<\/strong> <a href=\"https:\/\/providers.apievangelist.com\/providers\/mixrank\/\" target=\"_blank\" rel=\"noopener nofollow\">API Evangelist provider profile of MixRank<\/a>.<\/p>\n\n<h2>Clearbit (Now HubSpot Breeze Intelligence)<\/h2>\n\n<p><a href=\"https:\/\/predictleads.com\/blog\/clearbit-alternatives\/\">Clearbit<\/a> was an enrichment provider that HubSpot acquired in December 2023 and rebranded as Breeze Intelligence in September 2024, and it now lives inside the HubSpot CRM.<\/p>\n\n<h3>What They Offer<\/h3>\n<ul>\n<li>Company and contact enrichment on HubSpot records, drawn from 200M+ buyer and company profiles, per a <a href=\"https:\/\/databar.ai\/blog\/article\/databar-vs-clearbit-multi-provider-enrichment\" target=\"_blank\" rel=\"noopener nofollow\">Databar comparison<\/a>.<\/li>\n<li>Firmographic, technographic, and employee attributes on CRM records.<\/li>\n<li>Standard firmographic enrichment included with HubSpot Core Seats, with heavier enrichment drawing on HubSpot Credits.<\/li>\n<\/ul>\n\n<p><strong>Good for:<\/strong> Teams that manage their GTM motion in HubSpot and want enrichment on CRM records without adding a vendor.<\/p>\n\n<p><strong>Limitation:<\/strong> Third-party sources report that the legacy standalone Clearbit APIs are deprecated and that a paid HubSpot subscription is required, so an agent outside HubSpot cannot call it as a general company data API.<\/p>\n\n<p><strong>Bottom line:<\/strong> Breeze Intelligence fits HubSpot-first teams; PredictLeads works with any stack, HubSpot included, through API, flat files, webhooks, and MCP, starting with 100 free API requests.<\/p>\n\n<p><strong>Sources:<\/strong> <a href=\"https:\/\/databar.ai\/blog\/article\/databar-vs-clearbit-multi-provider-enrichment\" target=\"_blank\" rel=\"noopener nofollow\">Databar comparison of Clearbit and Breeze Intelligence<\/a>.<\/p>\n\n<h2>Feature Comparison Table of Company Data APIs for AI Agents<\/h2>\n\n<p>The table maps each provider to the six scoring dimensions. &#8220;Not documented&#8221; and &#8220;Not published&#8221; mean we could not confirm a capability or figure from public sources, not that it is definitely absent.<\/p>\n\n<div style=\"overflow-x:auto;margin:24px 0;\">\n<table style=\"width:100%;min-width:880px;border-collapse:collapse;font-family:'Poppins','Segoe UI',system-ui,sans-serif;font-size:14px;line-height:1.45;color:#3a2f4d;\">\n<caption style=\"caption-side:top;text-align:left;font-size:17px;font-weight:800;font-style:italic;color:#76508e;padding:0 0 10px;\">Company data APIs for AI agents, compared on six dimensions (October 2026)<\/caption>\n<thead>\n<tr style=\"background:#76508e;color:#ffffff;\">\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">Provider<\/th>\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">Signal breadth<\/th>\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">Structured event taxonomy<\/th>\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">Source-linked records<\/th>\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">Historical timestamps<\/th>\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">MCP server<\/th>\n<th scope=\"col\" style=\"padding:12px;text-align:left;border:1px solid #5b3d70;\">Self-serve API<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background:#efe4f6;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;color:#76508e;\">PredictLeads<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; jobs, tech, news, funding, connections, web changes<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; 37 News Event categories<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; source URL per detection and connection<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; back to 2016, with first_seen_at and last_seen_at<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; mcp.predictleads.com<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; 100 free API requests<\/td>\n<\/tr>\n<tr style=\"background:#ffffff;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;\">Coresignal<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; companies, employees, jobs<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; remote server, OAuth<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; freemium<\/td>\n<\/tr>\n<tr style=\"background:#f3ecf8;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;\">Crustdata<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; companies, people, jobs, social, web<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; watcher alerts<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; hosted, 40 tools<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; free starter credits<\/td>\n<\/tr>\n<tr style=\"background:#ffffff;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;\">Explorium<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; firmographic, technographic, contact<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; business events<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; AgentSource MCP<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published<\/td>\n<\/tr>\n<tr style=\"background:#f3ecf8;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;\">People Data Labs<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; person, company, IP, job postings<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published (monthly builds)<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; third-party connectors<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; free testing tier<\/td>\n<\/tr>\n<tr style=\"background:#ffffff;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;\">MixRank<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; technographics, mobile apps, companies, people<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not listed in profile<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Yes &#8211; freemium<\/td>\n<\/tr>\n<tr style=\"background:#f3ecf8;\">\n<th scope=\"row\" style=\"padding:12px;text-align:left;border:1px solid #ece0f5;\">Clearbit (Breeze Intelligence)<\/th>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Partial &#8211; firmographic and contact enrichment<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not documented<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">Not as a standalone server<\/td>\n<td style=\"padding:12px;border:1px solid #ece0f5;\">No &#8211; paid HubSpot required<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n<p>If your agent must cite evidence, read the source column before any scale number. For trade-offs between access paths, see our comparison of <a href=\"https:\/\/predictleads.com\/blog\/company-data-api-flat-files-webhooks-mcp\/\">API, flat files, webhooks, and MCP delivery<\/a>.<\/p>\n\n<h2>Why PredictLeads Is the Best Company Data API for AI Agents<\/h2>\n\n<p>The combination is the advantage. Coresignal and People Data Labs go deep on people records, Crustdata on live lookups, Explorium on accounts plus contacts, MixRank on mobile, and Breeze Intelligence on HubSpot-native enrichment. PredictLeads is the provider in this comparison built around company-level signals that an agent can stack, timestamp, and cite.<\/p>\n\n<p>An account-research agent can pull a Financing Events record, check Job Openings for new sales roles, look for Technology Detections that provide evidence of a competitor&#8217;s tool, and confirm a published integration in Connections, all keyed to one domain. Our post on an <a href=\"https:\/\/predictleads.com\/blog\/ai-agent-real-time-signals-lead-qualification\/\">AI agent that qualifies leads with real-time signals<\/a> shows the scoring logic.<\/p>\n\n<p>Good agents also hedge correctly. When a technology is no longer detected, the agent should say exactly that, because a script change, a recrawl gap, or a signature change can each cause the gap. A matching shift in job postings is stronger supporting evidence of a possible transition, not confirmation.<\/p>\n\n<p>PredictLeads is <a href=\"https:\/\/predictleads.com\/security\">SOC 2 Type II certified<\/a>, GDPR and CCPA compliant, and collects only publicly available information, with no personally identifiable information (PII) in its company intelligence datasets. If you are choosing an access layer specifically, our roundup of the <a href=\"https:\/\/predictleads.com\/blog\/best-mcp-servers-for-company-enrichment-in-2026\/\">best MCP servers for company enrichment<\/a> also covers platforms such as Clay and HubSpot.<\/p>\n\n<h2>Frequently Asked Questions<\/h2>\n\n<h3>What is the best company data API for AI agents in 2026?<\/h3>\n<p>PredictLeads is the best overall company data API for AI agents in this October 2026 comparison, because it combines hiring, technographic, news, funding, and relationship signals in one API with a native MCP server. Other providers are strong in narrower lanes, such as people records, live lookups, or HubSpot-native enrichment. PredictLeads covers 120M+ companies, and its records carry timestamps and source URLs an agent can cite.<\/p>\n\n<h3>How do I choose between PredictLeads, Coresignal, and Crustdata for an AI agent workflow?<\/h3>\n<p>Start from the question your agent must answer. Coresignal suits agents that reason over employee and job records, and Crustdata suits live lookups and watcher alerts on named people and companies. If the agent has to explain why an account matters now, choose categorized company signals with sources: PredictLeads returns News Events in 37 categories and 371.8M+ Connections, each with a <code>source_url<\/code>.<\/p>\n\n<h3>Do AI agents need an MCP server, or is a REST API enough?<\/h3>\n<p>A REST API is enough for fixed pipelines, but an MCP server lets an agent discover and call data tools on its own mid-conversation. In many cases, teams use both: MCP for exploratory agent work, and the API, flat files, or webhooks for scheduled jobs. PredictLeads exposes the same datasets at mcp.predictleads.com and through its REST API, so a prototype can become a pipeline without a provider change.<\/p>\n\n<h3>Can I prototype an AI agent on a company data API before committing to a contract?<\/h3>\n<p>Yes. Coresignal and MixRank list freemium entry points, People Data Labs has a free testing tier, and Crustdata offers free starter credits. PredictLeads gives you 100 free API requests per month, with no credit card and no sales call. After that, API usage is pay-as-you-go with a $40 minimum monthly charge, and the per-credit cost falls as volume grows.<\/p>\n\n<h3>How is a company data API different from a contact database for AI agents?<\/h3>\n<p>A company data API returns company-level signals such as hiring, technology evidence, events, funding, and relationships, while a contact database returns records about individual people. Agents use the first to decide which accounts matter and why, and the second to decide whom to contact. PredictLeads is a company data provider covering 120M+ companies, not a contact database. Our <a href=\"https:\/\/predictleads.com\/blog\/b2b-data-providers-ranked\/\">ranking of B2B data providers<\/a> covers contact vendors if you need both.<\/p>\n\n<h2>Ready to see this in your own data?<\/h2>\n<p>Get 100 free API requests per month &#8211; no credit card, no sales call.<\/p>\n<p><a href=\"https:\/\/predictleads.com\/sign_up?utm_source=blog&amp;utm_medium=cta&amp;utm_campaign=company-intelligence-api-comparison\" style=\"display:inline-block;background:#76508e;color:#ffffff;padding:12px 24px;border-radius:8px;font-weight:700;text-decoration:none;\">Create your free PredictLeads account<\/a><\/p>\n\n<h2>Final Thoughts on Choosing a Company Data API for AI Agents<\/h2>\n\n<p>Pick the provider whose records your agent can defend. Scale numbers make a vendor page look strong, but an agent&#8217;s output is only as credible as the timestamp and source on the record it retrieved. Start with one workflow, such as researching newly funded accounts, and test it against accounts you know well. Our guide to <a href=\"https:\/\/predictleads.com\/blog\/building-an-enrichment-agent-on-the-predictleads-mcp-server\/\">building an enrichment agent on the PredictLeads MCP server<\/a> is a practical starting point, and you can <a href=\"https:\/\/predictleads.com\/sign_up?utm_source=blog&amp;utm_medium=cta&amp;utm_campaign=company-intelligence-api-comparison-final\">start with 100 free API requests<\/a>.<\/p>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"Best Company Data APIs for AI Agents (October 2026)\",\n  \"description\": \"Company data APIs for AI agents compared for October 2026. See how PredictLeads, Coresignal, Crustdata, and People Data Labs score on signals, MCP, and sources.\",\n  \"datePublished\": \"2026-08-21T11:43:59+00:00\",\n  \"dateModified\": \"2026-10-21T09:00:00+00:00\",\n  \"author\": {\n    \"@type\": \"Organization\",\n    \"name\": \"PredictLeads\",\n    \"url\": \"https:\/\/predictleads.com\"\n  },\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"PredictLeads\",\n    \"url\": \"https:\/\/predictleads.com\"\n  },\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/predictleads.com\/blog\/company-intelligence-api-comparison\/\"\n  },\n  \"url\": \"https:\/\/predictleads.com\/blog\/company-intelligence-api-comparison\/\",\n  \"wordCount\": 2750,\n  \"keywords\": [\n    \"company data API for AI agents\",\n    \"best company data APIs for AI agents\",\n    \"company intelligence API comparison\",\n    \"company intelligence API\",\n    \"B2B data API for AI agents\",\n    \"MCP server company data\",\n    \"AI agent company data\",\n    \"buying signals API for AI agents\",\n    \"Coresignal alternatives\",\n    \"Crustdata alternatives\",\n    \"People Data Labs alternatives\",\n    \"Clearbit alternatives\"\n  ]\n}\n<\/script>\n\n\n================================================================\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is the best company data API for AI agents in 2026?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads is the best overall company data API for AI agents in this October 2026 comparison, because it combines hiring, technographic, news, funding, and relationship signals in one API with a native MCP server. Other providers are strong in narrower lanes, such as people records, live lookups, or HubSpot-native enrichment. PredictLeads covers 120M+ companies, and its records carry timestamps and source URLs an agent can cite.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How do I choose between PredictLeads, Coresignal, and Crustdata for an AI agent workflow?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Start from the question your agent must answer. Coresignal suits agents that reason over employee and job records, and Crustdata suits live lookups and watcher alerts on named people and companies. If the agent has to explain why an account matters now, choose categorized company signals with sources: PredictLeads returns News Events in 37 categories and 371.8M+ Connections, each with a source_url.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Do AI agents need an MCP server, or is a REST API enough?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"A REST API is enough for fixed pipelines, but a Model Context Protocol (MCP) server lets an agent discover and call data tools on its own mid-conversation. In many cases, teams use both: MCP for exploratory agent work, and the API, flat files, or webhooks for scheduled jobs. PredictLeads exposes the same datasets at mcp.predictleads.com and through its REST API, so a prototype can become a pipeline without a provider change.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I prototype an AI agent on a company data API before committing to a contract?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. Coresignal and MixRank list freemium entry points, People Data Labs has a free testing tier, and Crustdata offers free starter credits. PredictLeads gives you 100 free API requests per month, with no credit card and no sales call. After that, API usage is pay-as-you-go with a $40 minimum monthly charge, and the per-credit cost falls as volume grows.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How is a company data API different from a contact database for AI agents?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"A company data API returns company-level signals such as hiring, technology evidence, events, funding, and relationships, while a contact database returns records about individual people. Agents use the first to decide which accounts matter and why, and the second to decide whom to contact. PredictLeads is a company data provider covering 120M+ companies, not a contact database.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Which company data providers offer an MCP server in 2026?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"In this October 2026 comparison, Coresignal, Crustdata, Explorium, and PredictLeads document hosted MCP servers, while People Data Labs is reachable through third-party MCP connectors. MixRank's directory profile lists no MCP server, and Clearbit enrichment now sits inside HubSpot as Breeze Intelligence. 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