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Diego Costa
Diego Costa

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Building Autonomous SDR Swarms with Real-Time B2B Lead Enrichment MCP

#mcp #ai #automation

Building Autonomous SDR Swarms with Real-Time B2B Lead Enrichment MCP

The most efficient way to scale autonomous SDR agent swarms is by providing LLMs with direct, native access to live B2B firmographics and intent data via a Model Context Protocol (MCP) server. This architecture allows AI agents to perform real-time lead qualification and technographic research within their own context window, eliminating the latency and complexity of traditional middleware.

Core Features of the B2B Enrichment MCP

Building a production-grade sales agent requires more than just static data; it requires high-fidelity, schema-validated insights that an LLM can interpret without error. Our MCP server implements strict Zod-annotated parameters to prevent parameter hallucinations, ensuring that tools like Cursor, Claude Desktop, and custom autonomous swarms receive structured data every time.

  • Deep Firmographics: Access real-time data on company size, revenue brackets, headquarters location, and industry classification.
  • Technographic Intelligence: Identify the specific software stack a prospect is using to tailor outreach messaging autonomously.
  • Live Intent Signals: Trigger agent actions based on recent funding rounds, hiring surges, or leadership changes.
  • Strict JSON-RPC Compliance: Designed to work out-of-the-box with any MCP-host, ensuring seamless tool-calling and resource resolution.

Configuration for Claude Desktop and Cursor

To give your autonomous agents or local development environment access to live B2B data, add the following configuration to your claude_desktop_config.json or Cursor settings. This connects the Model Context Protocol directly to our high-performance worker.

{
  "mcpServers": {
    "b2b-enrichment": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-http",
        "--url",
        "https://lead-enrichment-mcp.agent-infra.workers.dev/mcp"
      ],
      "env": {
        "ENRICHMENT_API_KEY": "YOUR_FREE_API_KEY"
      }
    }
  }
}
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Standardized JSON-RPC Tool Response

When your agent invokes the enrich_lead tool, it receives a clean, structured payload. This allows the LLM to make logic-based decisions (e.g., "If industry is SaaS and employee count > 50, proceed to draft email").

{
  "method": "tools/call",
  "params": {
    "name": "enrich_lead",
    "arguments": {
      "domain": "stripe.com"
    }
  },
  "result": {
    "companyName": "Stripe",
    "industry": "Financial Services / Fintech",
    "employeeCount": 8000,
    "technographics": ["React", "AWS", "Salesforce", "Zendesk"],
    "intentSignals": {
      "recentFunding": "Series I",
      "hiringVelocity": "High"
    },
    "confidenceScore": 0.98
  }
}
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Risk-Free Metered Billing for AI Agents

One of the biggest hurdles in building autonomous SDR swarms is the cost of failed or low-quality data lookups. Our B2B Lead Enrichment MCP utilizes a Confidence-First Metered Billing model. Your account is only debited for successful enrichments that return a Confidence Score > 0.6. If the server returns a low-confidence match or a "not found" status, the cost of that query is $0. This allows you to scale high-volume agent discovery scripts without the risk of burning through your budget on stale or inaccurate data.

Get Started with the B2B Lead Enrichment MCP

Ready to empower your AI agents with real-time firmographic intelligence? You can get your production API key and explore the full documentation on our free tier today.

Visit lead-enrichment-mcp.agent-infra.workers.dev to grab your API key and start building autonomous SDR swarms.

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