Best B2B Data APIs for AI Agents and MCP (2026)

The best B2B data APIs for AI agents in 2026 return typed JSON, publish their limits and prices, and offer an MCP server that Claude or ChatGPT can sign in to. We compared ten B2B data APIs on what an agent needs: MCP support, sign-in for agents, structured output, freshness, cost per call and rate limits. Our picks: the Autobound Signal API for live company and people signals, People Data Labs and Coresignal for raw person and company records, and Apollo, ZoomInfo or Lusha when the agent mostly needs contact data.

Quick answer: Pick a B2B data API for an AI agent on five checks: (1) a hosted MCP server with OAuth sign-in, (2) a stable JSON schema, (3) a published freshness window, (4) a published price per call, and (5) a published rate limit with a clear 429 response. In November 2026, Autobound, Apollo, ZoomInfo, Coresignal, Crustdata, PredictLeads and Lusha all run hosted MCP servers. They differ most in what data they return and how they bill.

AI agents use APIs differently from people in a dashboard. A rep looks up a few dozen companies a day. An agent can loop over a full account list in one run. It parses every field without a human to catch a type change, and it pays for every call it makes. This guide looks at B2B data APIs through that lens.

Why B2B Data APIs for AI Agents Are Different

An AI agent is a program that decides what to do next from an API response. Nobody reads the response before the agent acts on it. That creates five needs that many B2B APIs built for dashboards do not meet.

1. Schema determinism

A missing field, a string where a number should be, or an unexpected null breaks an agent's parser. Agent-grade APIs publish an OpenAPI spec, keep field names stable and return explicit nulls.

2. Push delivery over polling

An agent that polls an API every few minutes wastes calls and credits. Bulk files and push delivery (cloud storage drops, webhooks, flat files) let a pipeline start the agent only when new data lands.

3. Machine-readable errors

A 429 with a Retry-After header tells the agent when to try again. A 500 with an HTML page tells it nothing. Good APIs return a JSON error with a code, a message and, where it helps, a fix.

4. Rate limits designed for burst patterns

Agents send calls in bursts: 500 companies, then nothing, then another 500. A published per-minute limit lets you size the burst. An unpublished limit means you find it in production.

5. Real-time freshness vs. snapshot databases

Many B2B databases refresh on a monthly or quarterly cycle. For a question like "did this account just raise money?", a 30-day-old snapshot gives the wrong answer. Ask each vendor how many days pass from the event to the API, per data type.

What an Agent Should Check Before It Calls a Data API

Give your agent, or the person who builds it, this checklist. Each item has a yes or no answer in the vendor's public docs.

• Freshness: How old is the newest record of each type? Does the response carry a date per record (for example detected_at), so the agent can drop stale data?
• Coverage: Does the API return the entity types you need (companies, people, events)? What does it return for an unknown domain: an empty result, a 404 or a guess?
• Cost per call: What does one call cost, in credits and in dollars? Is a call with zero results free? Can the agent read its balance from the API?
• Rate limits: What is the published limit per minute? Do all keys share it? Does a 429 come with a Retry-After header?
• Structured output: Is there an OpenAPI spec? Are errors JSON with a code? Are filter values a closed list that the agent can look up first?
• Agent sign-in: Does the MCP server use OAuth, so each person signs in with their own account? Is there an API key option for headless scripts?

How We Evaluated Each API

We read each vendor's public pricing page, API docs and MCP docs on November 5, 2026. We list only the facts that those pages state. The sources are at the end of this article.

The Best B2B Data APIs for AI Agents in 2026

1. Autobound Signal API: Best for Live Company and People Signals

Base URL: https://signals.autobound.ai | Signal types: 35, with 700+ subtypes | MCP: https://mcp.autobound.ai/mcp, 25 tools | Delivery: REST API, MCP, GCS push, flat file | Docs: autobound-api.readme.io

The Autobound Signal API returns events about companies and people: news, hiring trends, job changes, SEC filings, Form D funding, earnings calls, website changes, Reddit and podcast mentions, and more. Browse every type in the signal catalog. Each signal has a type, a subtype, a detected_at date and the company or person it belongs to.

What makes it agent-grade:

• Typed responses: Every signal uses the same envelope (signal_type, signal_subtype, detected_at, company, contact, data). Enrich responses add a coverage block and a warnings list, so the agent knows why a filter returned less.
• Closed filter lists: Unknown industry, size or seniority values return a 400 with suggestions. The free getFilters tool returns the exact values first.
• Published prices: 2 credits per company or contact that search returns, 2 credits per signal that enrich returns. A call with zero results is free. Credit packs put that at $0.02 to $0.008 per result.
• Published limits: 300 requests per minute per customer, shared by all keys. Search returns up to 100 results per call, enrich up to 500 signals.
• Push and bulk: GCS push and flat files deliver the same signals in bulk, for pipelines that should not poll.

Example request and response (response shape from the OpenAPI spec; values are examples):

curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain": "stripe.com", "signal_types": ["news"], "limit": 20}'

{
  "company": { "name": "Stripe", "domain": "stripe.com", "linkedin_url": "https://linkedin.com/company/stripe" },
  "signals": [
    {
      "signal_id": "sig_abc123",
      "signal_type": "news",
      "signal_subtype": "partners_with",
      "detected_at": "2026-09-22T12:00:00Z",
      "association": "company",
      "company": { "name": "Stripe", "domain": "stripe.com" },
      "contact": null,
      "data": {
        "title": "Article headline",
        "summary": "One-sentence summary of the event",
        "source_url": "https://example.com/article",
        "published_at": "2026-09-21T00:00:00Z"
      }
    }
  ],
  "signal_summary": { "news": 1 },
  "coverage": { "total_after_filters": 1, "signal_types_available": ["news"] },
  "warnings": []
}

Limitations:

• The product is signals. If the agent needs a large contact database for phone numbers, pair it with a contact data vendor.
• Search looks back 50 days by default. A subtype filter on some high-volume types, or an entity filter, cuts the window to 14 days.
• Push delivery is GCS push and flat files. There is no per-event callback to your endpoint.

Pricing: 1,000 free credits at signup. Credit packs start at $19 for 1,900 credits. See pricing.

Best for: Agents that must know what changed at an account (a funding round, a new executive, a hiring push, a job change) and cite the source.

2. People Data Labs: Best for Raw Person Records at Scale

Endpoint: api.peopledatalabs.com | Data: person and company records | Delivery: APIs, bulk data, data licensing

People Data Labs (PDL) sells person data through APIs, bulk data and data licensing. It is a good fit for teams that build a product on person records and want to own the logic on top.

• Pricing: Free plan with up to 100 credits a month. Pro starts at $98 a month. Enterprise (bulk data) is custom.
• MCP: PDL's pricing page does not list an MCP server. The PDL MCP servers we found are third-party wrappers.
• Limits for agents: PDL returns records, not events. Contact data is not part of the free plan.

Best for: Data teams that need person and company records and will build the signal layer themselves.

3. Apollo.io API: Best All-in-One API for Simple Agent Workflows

MCP: https://mcp.apollo.io/mcp (Streamable HTTP) | Data: Apollo states 240M contacts and 30M companies

Apollo is a sales platform with a contact database, email sequencing and pipeline tools. Its hosted MCP server signs in with OAuth 2.0. Headless clients can send a master API key in the X-Api-Key header.

• Pricing: A free plan, paid plans by seat with credits, and Custom plans. The pricing page says API access is on Custom plans.
• MCP limits: Apollo's docs say free accounts registered with a personal email cannot use people or company search and enrichment. Tool access can vary by plan.
• Limits for agents: The data is contacts and firmographics. It is not an event feed.

Best for: Agents that find contacts and enroll them in sequences inside one tool.

4. ZoomInfo (GTM.AI): Largest Contact Database, Account Required

MCP: https://mcp.zoominfo.com/mcp | Data: ZoomInfo states 500M+ contacts and 100M+ companies, plus intent and behavioral signals

ZoomInfo sells headless access through GTM.AI. Its MCP server works with Claude, ChatGPT, Perplexity, Claude Code, Cursor and other clients that support Streamable HTTP and OAuth. Each person signs in with their own ZoomInfo login.

• Account: The MCP needs a ZoomInfo account with data credits. The GTM.AI docs list a free account with 1,000 data credits and 1,000 AI credits. Your plan's API limits and credits apply to MCP use.
• Pricing: Free to start, then consumption credits based on usage.
• Limits for agents: Some older subscriptions have only recurring monthly credits, which the MCP cannot use.

Best for: Teams that already pay for ZoomInfo and want the same data inside Claude or ChatGPT.

5. Clearbit (now HubSpot): Enrichment Inside HubSpot

Clearbit has joined HubSpot. Its data now enriches contact and company records inside the HubSpot CRM: lead, contact and account data, industry codes, corporate hierarchies and IP-based visitor intent.

• Pricing: Sold through HubSpot. HubSpot states that data enrichment does not consume HubSpot Credits.
• Limits for agents: It is built for records inside HubSpot, not for an agent outside it.

Best for: HubSpot users whose agents work on HubSpot records.

6. Coresignal: Best Raw Company, Employee and Job Data

MCP: https://mcp.coresignal.com/mcp/v2 (Streamable HTTP, OAuth 2.1) | Data: Coresignal states 70M+ company records, 907M+ employee profiles and 482M+ job postings

Coresignal sells public web data on companies, employees and job postings as APIs and datasets. Its MCP server has five tools: entity_search, entity_fields, entity_fetch, email_enrich and artifact_read.

• MCP prices: entity_search costs 20 credits per search, entity_fetch 1 to 20 credits per record and email_enrich 10 credits per email. entity_fields and artifact_read are free. MCP calls use the same credit balance as the API.
• Pricing: API plans from $49 a month (Mini) to $5,000 a month (Elite), with a 7-day free trial. Datasets are priced by agreement.
• Limits for agents: The data is raw records. The agent or your pipeline must derive events from them.

Best for: Teams that build their own intelligence layer on large raw datasets.

7. Crustdata: Company, People, Jobs and Social Data for Agents

MCP: https://install.crustdata.com/mcp

Crustdata's MCP tools find and enrich companies, search and enrich people (with business and personal emails and phone numbers), search job listings, track social posts and read web content. Clients such as claude.ai and Codex sign in with a Crustdata account. Code can send a Crustdata API key as a bearer token.

Best for: Agents that need people, company, job and social data from one MCP server.

8. PredictLeads: Company Events With Pay-As-You-Go Pricing

MCP: https://mcp.predictleads.com/ | Delivery: REST API, webhooks, flat files (JSONL through S3, GCS or SFTP), MCP

PredictLeads lists 11 datasets, including news events, job openings, technology detections, financing events, key customers and GitHub repositories.

• Pricing: Up to 100 free API calls a month. Then $0.04 per credit with a $40 monthly minimum, falling to $0.002 per credit above 500,000 calls.
• Limits for agents: After the monthly limit, requests return a 402. Unused credits do not roll over.

Best for: Agents that need company events and technology data at a low unit price.

9. Lusha API: Contact Enrichment With an MCP Server

MCP: https://mcp.lusha.com

Lusha sells contact and company data. Its MCP server signs in with OAuth in Claude, ChatGPT and Codex. Other clients (Cursor, VS Code, Gemini CLI, n8n) send an API key in the x-api-key header. MCP calls use the same credit pool as the REST API.

• Pricing: Free plan with 40 credits a month. Starter is $49.90 a month (400 credits), Pro $69.90 (600 credits) and Premium $399.90 (3,400 credits) on monthly billing. Scale is custom.
• Limits for agents: Contact data only. No event feed.

Best for: Agents that add an email and phone to a known person.

10. Clay: Orchestration Layer Over Many Data Sources

Clay is not a single data source. It runs waterfall enrichment across many providers in one table, and adds AI research steps on top.

• Pricing: A free plan with 100 data credits a month. The pricing header lists Launch at $167 a month and Growth at $446 a month.
• Agent access: HTTP API integrations and webhook automation are on Growth and Enterprise, not on Launch. The pricing page does not list an MCP server.
• Limits for agents: The output schema depends on which providers returned data, so the agent needs defensive parsing.

Best for: Teams that already build lists in Clay and want an agent to call those workflows.

B2B Data API Comparison for AI Agents

"Not listed" means the vendor's public pages that we read on November 5, 2026 did not state it. "Not checked" means we did not find a public page for it.

Add Autobound to Claude or ChatGPT (MCP)

The Autobound MCP server is at https://mcp.autobound.ai/mcp (Streamable HTTP). Clients sign in with OAuth through the Autobound portal. The address is not a secret. A free account has 1,000 credits. The full guide for every client is on the Autobound MCP page.

Claude (claude.ai, Desktop and mobile)

1. In Claude, open Settings, then Connectors.
2. Click Add custom connector. Name it Autobound and paste https://mcp.autobound.ai/mcp.
3. Click Add, then sign in to Autobound and click Allow.

One connector works on claude.ai, Claude Desktop and the Claude mobile app.

ChatGPT

1. In ChatGPT, open Settings, then Connectors. Turn on developer mode.
2. Click Create. Name it Autobound and paste https://mcp.autobound.ai/mcp.
3. Set Authentication to OAuth. Leave the client ID and secret empty. Sign in to Autobound and click Allow.

Claude Code and Cursor

For Claude Code, run this command, then run /mcp, select autobound and sign in. Add --scope user to use it in all projects.

claude mcp add --transport http autobound https://mcp.autobound.ai/mcp

For Cursor, add this block to ~/.cursor/mcp.json, then click Login next to autobound in Cursor settings.

{
  "mcpServers": {
    "autobound": {
      "url": "https://mcp.autobound.ai/mcp"
    }
  }
}

Scripts and headless agents

Tools that cannot open a sign-in window send a Signal API key from the portal as a Bearer token on every request. Treat the key like a password: anyone with it can spend your workspace credits.

URL:    https://mcp.autobound.ai/mcp
Header: Authorization: Bearer YOUR_API_KEY

The old SSE address https://mcp.autobound.ai/sse works with an API key until December 1, 2026. Use the /mcp address for new setups.

The tools an agent gets

• listSignalTypes (free): every signal type with a description and the date of its newest signal.
• getFilters (free): the exact industry, size, revenue, seniority and department values that search accepts.
• searchCompanies and searchContacts: companies or people with recent signals. 2 credits per result.
• enrichCompany and enrichContact: every signal for one company (by domain) or one person (by email or LinkedIn URL). 2 credits per signal.
• getSignalById (free): one signal in full.
• getCredits (free): the credit balance, so the agent can check its budget.

The server also has buyer intent, email verification and monitoring tools. Try a prompt such as "Which fintech companies announced layoffs in the last 30 days?" The assistant calls getFilters first, then searchCompanies with the matching signal types and industry.

Architecture Patterns: How to Connect B2B Data APIs to AI Agents

How you connect the API matters as much as which API you pick. Three patterns cover most production agent systems.

Pattern 1: Direct tool call (simplest)

The agent calls the REST API as a tool. This works when the agent needs data during one task.

// Agent tool definition
const getCompanySignals = {
  name: "get_company_signals",
  description: "Get recent signals for one company by domain",
  parameters: {
    type: "object",
    properties: {
      domain: { type: "string", description: "Company domain, e.g. stripe.com" },
      signal_types: {
        type: "array",
        items: { type: "string" },
        description: "Signal types, e.g. news, hiring-trends, sec-8k"
      }
    },
    required: ["domain"]
  },
  execute: async ({ domain, signal_types }) => {
    const response = await fetch("https://signals.autobound.ai/v1/companies/enrich", {
      method: "POST",
      headers: { "X-API-KEY": API_KEY, "Content-Type": "application/json" },
      body: JSON.stringify({ domain, signal_types, limit: 20 })
    });
    return response.json();
  }
};

When to use: One agent that looks up data on request, at low volume.

Pattern 2: MCP server (standardized)

Connect a hosted Model Context Protocol server. Any MCP client (Claude, ChatGPT, Cursor) then gets the vendor's tools with no custom code. The setup steps above take a few minutes. For building your own MCP server over a REST API, see our MCP server guide.

When to use: Chat assistants, developer tools, and teams that want each person to sign in with their own account.

Pattern 3: Event-driven pipeline (production-grade)

Use bulk delivery (GCS push or flat files) to land new signals in your own storage. A function starts the agent when a new file arrives, so nothing polls.

// New signal file lands in your GCS bucket -> Cloud Function -> agent
export async function processNewSignals(event: CloudEvent) {
  const { bucket, name } = event.data;
  const signals = await readSignalFile(bucket, name);

  for (const signal of signals) {
    // Route only the types this agent acts on
    if (["news", "sec-8k", "work-milestones"].includes(signal.signal_type)) {
      await publishToAgent({
        company: signal.company,
        signal_type: signal.signal_type,
        signal_subtype: signal.signal_subtype,
        detected_at: signal.detected_at,
        data: signal.data
      });
    }
  }
}

When to use: Production systems that process thousands of signals a day.

What About MCP (Model Context Protocol)?

The Model Context Protocol is the standard way for AI assistants to discover and call external tools. Anthropic introduced it, and Claude, ChatGPT, Cursor and many other clients now support it.

In April 2026, few B2B data vendors ran their own MCP server. By November 2026, most of the vendors above do: Autobound, Apollo, ZoomInfo, Coresignal, Crustdata, PredictLeads and Lusha. Cognism also lists MCP and API access on its Standard and Pro plans. The servers now differ on three points:

• Sign-in: OAuth per person, an API key, or both.
• Billing: Most MCP calls draw on the same credits as the API. Check which tools are free.
• Data: Records (who works where) or events (what changed). An agent that writes outreach or flags risk needs events.

Decision Framework: Which API for Which Agent Architecture

For a wider list of vendors by data type, see our guide to B2B data providers.

Frequently Asked Questions

What is a B2B data API?

A B2B data API gives software access to business data: company firmographics, contact data, technographics, buying signals and business events. Unlike a dashboard, an API lets a program, such as an AI agent, query the data directly.

Which B2B data APIs work best with AI agents?

APIs with a hosted MCP server, typed JSON, published prices and published rate limits. For events about accounts, use the Autobound Signal API. For raw person and company records, People Data Labs and Coresignal fit. For contact data inside Claude or ChatGPT, Apollo, ZoomInfo and Lusha run hosted MCP servers.

How do I connect a B2B data API to Claude or ChatGPT?

Add the vendor's MCP server as a custom connector. For Autobound, open Settings, then Connectors, add https://mcp.autobound.ai/mcp, sign in and click Allow. In ChatGPT, turn on developer mode first and set Authentication to OAuth. The MCP setup page has the steps for each client.

Do I need real-time signals or is a static database enough?

It depends on the job. To fill in CRM records, a static database works. To find accounts where something just changed, such as job changes, hiring trends or company news, the agent needs dated signal data. Many production systems use both.

What is MCP and why does it matter for B2B data APIs?

Model Context Protocol (MCP) is a standard that lets AI assistants discover and call external tools. With a hosted MCP server, an agent uses a B2B data API with no custom integration code. In November 2026, most large B2B data vendors run one, including Autobound.

How much does a B2B data API cost for agent workloads?

Free tiers range from 40 credits a month (Lusha) to 1,000 credits at signup (Autobound). Paid entry points include $49 a month for Coresignal's Mini API plan, $98 a month for PDL Pro and $19 for Autobound's smallest credit pack. Agents make many calls, so compare the price per result and check whether empty results are free.

Can I use multiple B2B data APIs in one agent?

Yes. A common setup has a signal layer (Autobound) for events, an enrichment layer (PDL, Coresignal or a contact vendor) for records, and an orchestration layer (Clay or your own code) to combine them. With MCP, one assistant can hold several connectors at once.

The Bottom Line

Most B2B data APIs were built for people, not agents. In 2026, the gap is smaller: most large vendors run a hosted MCP server. The real differences now are the data (records or events), the price per call and how clearly the vendor publishes its limits.

For teams that build AI agents on B2B data, a practical stack is:

• Signal layer: the Autobound Signal API for dated events on companies and people, through REST, MCP or GCS push
• Enrichment layer: People Data Labs or Coresignal for raw records, or a contact vendor for emails and phones
• Orchestration layer: Clay or your own code when you combine many providers

To start, add Autobound to Claude or ChatGPT, read the Signal API docs, or talk to sales about GCS push and flat files.

Sources

We read each page on November 5, 2026. Prices and features change, so check the vendor page before you buy.

• People Data Labs pricing: peopledatalabs.com/pricing/person
• Apollo pricing: apollo.io/pricing. Apollo MCP: docs.apollo.io/docs/apollo-mcp
• ZoomInfo: zoominfo.com/about. ZoomInfo MCP: docs.gtm.ai/docs/connect-to-zoominfo-mcp and gtm.ai/docs/mcp
• Clearbit: clearbit.com. HubSpot data enrichment: knowledge.hubspot.com
• Coresignal pricing: coresignal.com/pricing. Coresignal MCP: docs.coresignal.com/mcp/coresignal-mcp
• Crustdata MCP: docs.crustdata.com/for-agents/mcp
• PredictLeads: predictleads.com and predictleads.com/pricing
• Lusha pricing: lusha.com/pricing. Lusha MCP: docs.lusha.com/mcp/overview
• Clay pricing: clay.com/pricing
• Cognism pricing: cognism.com/pricing
• Autobound: Signal API, MCP setup, pricing

Last updated: November 5, 2026.