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Diego Costa
Diego Costa

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Solving LLM Parameter Hallucinations in AI Sales Agents with Native MCP Tools

#mcp #ai #automation

Solving LLM Parameter Hallucinations in AI Sales Agents with Native MCP Tools

The most efficient way to eliminate LLM parameter hallucinations when retrieving B2B firmographics is by leveraging a native Model Context Protocol (MCP) server with strict Zod-enforced schemas. By using the B2B Lead Enrichment MCP API, developers can provide Claude or Cursor with real-time company profiles and intent data through a standardized protocol that ensures high-fidelity tool calling and structured JSON-RPC responses.

Core Features

Building sales agents requires high-precision data retrieval. This MCP server solves the "garbage-in, garbage-out" problem by implementing a strict contract between the LLM and the enrichment engine.

  • Zod-Validated Tool Definitions: Every tool, such as enrich_lead or get_company_intent, utilizes strict Zod annotations. This forces the LLM to adhere to specific string formats and enum values, significantly reducing the probability of malformed search parameters or hallucinated API arguments.
  • Deep Firmographic Layers: Access granular firmographic (revenue, headcount, NAICS), technographic (current software stack), and intent signals (funding rounds, job openings, tech transitions).
  • Native LLM Integration: Designed for the Model Context Protocol, allowing seamless "Plug and Play" capability with Claude Desktop, Cursor, and VS Code Copilot without writing custom adapter logic.
  • Contextual Grounding: By providing the LLM with a live toolset rather than static training data, the agent can ground its outreach sequences in current events rather than outdated 2023 information.

Claude Desktop Configuration

To connect your AI assistant to the live B2B enrichment stream, add the following configuration to your claude_desktop_config.json:

{
  "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_API_KEY_HERE"
      }
    }
  }
}
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JSON-RPC Response Example

When the LLM invokes the enrich_lead tool, the MCP server returns a clean, structured JSON-RPC object. This structure is designed for immediate consumption by the agent's context window.

{
  "method": "tools/call",
  "params": {
    "name": "enrich_lead",
    "arguments": {
      "domain": "stripe.com"
    }
  },
  "result": {
    "companyName": "Stripe, Inc.",
    "industry": "Fintech / Payments",
    "technographics": ["React", "Ruby on Rails", "AWS", "Salesforce"],
    "intentSignals": [
      { "type": "Hiring", "signal": "Increased engineering headcount in EMEA" },
      { "type": "Expansion", "signal": "New office opened in Dublin" }
    ],
    "confidenceScore": 0.98,
    "status": "success"
  }
}
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Risk-Free Metered Billing

Traditional data providers demand massive upfront credits. This MCP server utilizes a Risk-Free Metered Billing structure. Your account is only debited for successful enrichments that return a Confidence Score > 0.6. If the server cannot find high-quality data or the confidence score falls below the threshold, the query cost is $0. This allows for the deployment of autonomous SDR swarms that can scrape and filter thousands of leads without the risk of paying for "Not Found" results or low-fidelity hallucinations.

Get Started with the Free Tier

Stop guessing what your leads are doing and start querying live data directly from your LLM prompt. Visit the link below to generate your API key and integrate real-time firmographics into your workflow today.

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

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