To provide AI-powered IDE agents like Cursor or VS Code Copilot with native, real-time B2B firmographic and technographic data without writing custom

To provide AI-powered IDE agents like Cursor or VS Code Copilot with native, real-time B2B firmographic and technographic data without writing custom middleware or copy-pasting browser tabs, you must implement a Model Context Protocol (MCP) server. By integrating the B2B Lead Enrichment MCP API, you bridge the gap between your local development environment and live market intelligence, allowing your agent to query company tech stacks, headcount growth, and intent signals directly through a structured JSON-RPC interface.

Why REST is Failing Your IDE Agents

Traditional REST API integrations are brittle for LLMs because they rely on documentation that the model must "infer." This often leads to parameter hallucination—where an agent guesses a field name like company_name when the API requires domain.

The B2B Lead Enrichment MCP API solves this by using Zod-backed semantic schemas. When you connect this server to Cursor, the LLM receives a formal definition of the enrich_lead tool. It knows exactly which fields are required (domain) and which are optional (attributes, requiredFields). Because the tool definitions are part of the Model Context Protocol, the agent treats the API as a native capability rather than an external guessing game.

Integrating with Cursor and VS Code

Cursor's support for MCP allows you to augment your coding agent with "firmographic awareness." This is critical for developers building CRM integrations, lead scoring algorithms, or personalized outbound tooling.

To give your Cursor agent access to real-time B2B data, navigate to Settings > Models > MCP and add a new resource:

• Name: LeadEnrichment
• Type: http
• URL: https://lead-enrichment-mcp.agent-infra.workers.dev/mcp
• Headers:

{
  "Authorization": "Bearer YOUR_API_KEY_HERE"
}

Once configured, you can simply ask your agent: "Analyze the domain 'stripe.com' and tell me what cloud infrastructure they use so I can draft the AWS migration module."

High-Fidelity Data Extraction

The server returns deeply nested firmographic and technographic data. Crucially, the engine uses a Confidence Score threshold (> 0.6). Under its metered billing model, you are only charged for high-confidence matches, ensuring your agent isn't hallucinating data based on stale cache hits.

Here is an example of the schema-mapped JSON response your agent receives:

{
  "domain": "acme-industrial.io",
  "firmographics": {
    "name": "Acme Industrial",
    "headcount": "500-1000",
    "estimated_revenue": "$50M-$100M",
    "hq": "Chicago, IL"
  },
  "technographics": {
    "cloud": ["AWS", "GCP"],
    "frontend": ["React", "Next.js"],
    "crm": ["Salesforce"],
    "analytics": ["Segment", "Amplitude"]
  },
  "intent_signals": {
    "hiring": "Active - 3 Sales VPs in Q2 2026",
    "expansion": "New EMEA office announced"
  },
  "confidence_score": 0.94
}

Building Risk-Free Agent Swarms

One of the primary friction points in AI infrastructure is the cost of "runaway agents"—loops where an LLM calls an API 1,000 times by mistake. The B2B Lead Enrichment MCP API mitigates this via its Zero-Risk Metered Billing.

1. Free Tier: You get 100 enrichments for $0 to test your agent logic.
2. Quality Guarantee: Only responses with a confidence score greater than 0.6 are deducted from your quota.
3. Pro Scaling: At $49/mo for 10,000 enrichments, the cost per lead ($0.0049) is significantly lower than legacy providers like ZoomInfo or Clearbit, making it viable for high-volume agentic workflows.

Get Started in 60 Seconds

Stop building in a vacuum. Give your AI agents the real-world data they need to build context-aware B2B applications.

1. Visit lead-enrichment-mcp.agent-infra.workers.dev to generate your API key.
2. Plug the URL into your MCP-compatible host (Claude Desktop, Cursor, or your own MCP client).
3. Start querying live B2B intelligence natively within your LLM prompts.