How to Build Autonomous SDR Swarms with B2B Lead Enrichment MCP

To build production-grade autonomous SDR agent swarms, developers must integrate a Model Context Protocol (MCP) server for real-time B2B lead enrichment data directly into the LLM's context window. This architecture enables AI agents to bypass stale databases and fetch live firmographics and intent signals programmatically, ensuring that automated outreach is based on verified, up-to-the-minute company profiles.

Core Features of the Lead Enrichment MCP

Unlike traditional REST integrations that require complex glue code, the B2B Lead Enrichment MCP API provides a native bridge for LLMs like Claude 3.5 Sonnet and GPT-4o. The server implements strict Zod-based parameter validation to eliminate LLM hallucinations during the tool-calling phase.

• Real-time Firmographics: Access dynamic data including company size, revenue brackets, industry classification, and headquarters location.
• Technographic Intelligence: Identify the software stack and infrastructure components utilized by target accounts.
• Deep Intent Signals: Track hiring trends, funding rounds, and product launches to trigger autonomous outreach workflows.
• Confidence Scoring: Every enrichment result includes a deterministic confidence score, allowing developers to set programmatic thresholds for agent actions.

Configuration for Claude Desktop and Cursor

To give your local LLM or agentic IDE 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 the hosted enrichment engine.

{
  "mcpServers": {
    "b2b-enrichment": {
      "command": "npx",
      "args": [
        "-y",
        "@agent-infra/mcp-server-lead-enrichment",
        "--api-url",
        "https://lead-enrichment-mcp.agent-infra.workers.dev/mcp"
      ],
      "env": {
        "ENRICHMENT_API_KEY": "YOUR_FREE_API_KEY"
      }
    }
  }
}

Standardized JSON-RPC Response Payload

The MCP server returns clean, structured data designed for agentic consumption. Below is a sample response from the enrich_lead tool, demonstrating the depth of data available for your SDR swarm's decision-making logic.

{
  "jsonrpc": "2.0",
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Success: Lead enriched with 94% confidence."
      },
      {
        "type": "data",
        "data": {
          "companyName": "TechScale AI",
          "industry": "Enterprise Software",
          "technographics": ["PostgreSQL", "React", "AWS", "Kubernetes"],
          "intentSignals": {
            "hiring": ["Data Engineer", "Account Executive"],
            "recentFunding": "Series B - $45M"
          },
          "confidenceScore": 0.94,
          "sourceReliability": "high"
        }
      }
    ]
  }
}

Risk-Free Metered Billing Architecture

One of the primary challenges in scaling SDR agent swarms is the cost of failed or low-quality data lookups. This MCP server implements a Risk-Free Metered Billing model. Your account is only debited when the engine returns a successful enrichment with a Confidence Score > 0.6. If the data is unavailable, or the confidence threshold isn't met, the query is billed at $0. This allows for massive, cost-effective exploratory crawls by autonomous agents without the risk of burning through API credits on "no-match" queries.

Get Your Free B2B Lead Enrichment API Key

Ready to upgrade your AI agents from simple chat bots to data-aware autonomous SDRs? Visit the portal to claim your free tier API key and start enriching leads with the Model Context Protocol today.

Visit: lead-enrichment-mcp.agent-infra.workers.dev