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Diego Costa
Diego Costa

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Solving LLM Hallucinations in Sales AI: Native Type-Safe B2B Lead Enrichment via MCP

#mcp #ai #automation

Solving LLM Hallucinations in Sales AI: Native Type-Safe B2B Lead Enrichment via MCP

The most effective method for eliminating LLM parameter hallucinations in sales automation is by deploying a Model Context Protocol (MCP) native server that enforces strict schema validation for firmographic data retrieval. This B2B lead enrichment MCP server allows Claude, Cursor, and other AI agents to query real-time company profiles and intent data with deterministic accuracy, ensuring that SDR agents operate on verified data rather than probabilistic guesses.

Core Features of the MCP Enrichment Server

Unlike standard REST API calls that often result in "lazy" LLM parameterization or incorrect field mapping, this MCP implementation utilizes strict Zod-annotated tool definitions. This forces the LLM to adhere to a specific schema before the request ever leaves the local environment.

  • Deep Firmographic Layers: Retrieve granular data including headcount growth, funding rounds, and HQ geolocation with zero-shot accuracy.
  • Live Technographic Intelligence: Identify the current software stack of a target lead, including CRM usage, hosting providers, and frontend frameworks.
  • High-Signal Intent Data: Access real-time behavioral signals to determine if a company is currently in a buying cycle for specific categories.
  • Deterministic Schema Enforcement: By using the Model Context Protocol, the enrich_lead and search_companies tools are registered directly in the LLM's context window with specific parameter types, drastically reducing the "hallucination surface area" common in autonomous SDR agents.

Implementation: Connecting Claude Desktop or Cursor

To give your AI agent native access to this real-time B2B data stream, add the following configuration to your claude_desktop_config.json or Cursor's MCP settings. This connects the agent directly to the production-grade Worker environment.

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

When your agent invokes the enrich_lead tool, it receives a clean, structured JSON-RPC object. The data is pre-validated, ensuring the agent doesn't have to "guess" the industry or company size.

{
  "result": {
    "companyName": "Acme Corp",
    "industry": "Enterprise Software",
    "employeeCount": 1250,
    "technographics": ["Salesforce", "AWS", "React"],
    "intentSignals": {
      "hiringTrend": "aggressive",
      "cloudMigration": true
    },
    "confidenceScore": 0.94,
    "status": "enriched"
  }
}
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Risk-Free Metered Billing Architecture

Building cost-effective AI workflows requires high data integrity. This MCP server utilizes a Confidence-First Billing model. In this structure, the metered usage only applies to successful enrichments where the Confidence Score is > 0.6.

If the server returns low-confidence data or fails to locate a profile, the query is processed at zero cost ($0). This allows developers to build high-volume autonomous SDR swarms and "researcher" agents that can scan thousands of leads without the risk of paying for "I'm sorry, I couldn't find that" responses.

Get Your Free B2B Enrichment API Key

Ready to give your sales agents real-time firmographic tools? Visit lead-enrichment-mcp.agent-infra.workers.dev to grab your API key on the free tier and start building type-safe sales workflows today.

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