Solving LLM Parameter Hallucinations with Native B2B Lead Enrichment MCP
To eliminate parameter hallucinations in AI sales agents, developers must utilize a Model Context Protocol (MCP) server that enforces strict schema validation for B2B lead enrichment and real-time firmographic data. By connecting LLMs directly to an MCP-native API, you provide the model with a grounded tool definition that ensures high-fidelity data retrieval across company profiles, technographics, and intent signals without the risks of "creative" data invention.
Core Features
The B2B Lead Enrichment MCP server leverages the Model Context Protocol to expose typed tools directly to your LLM's orchestration layer. This approach solves the common problem where models guess company sizes or tech stacks by forcing the agent to fetch verified data through a structured interface.
- Strict Zod Schema Validation: Every tool parameter is strictly typed, preventing the LLM from passing malformed search queries or invalid domains.
- Multi-Layered Data Retrieval: Access comprehensive Firmographic (headcount, revenue, industry), Technographic (current software stack), and Intent Signals (hiring trends, news events) in a single request.
- Zero-Hallucination Grounding: Because the MCP server provides the context window with fresh, real-time data, the model stops relying on its outdated training data for company specifics.
Configuration for Claude Desktop and Cursor
To give your LLM native access to these tools, add the following configuration to your claude_desktop_config.json or your Cursor MCP settings. This connects the agent directly to our production-grade enrichment engine.
{
"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"
}
}
}
}
JSON-RPC Enrichment Response Example
When the LLM calls the enrich_lead tool, it receives a clean, structured JSON-RPC response. This allows the model to reason about the lead based on factual data rather than probabilistic guesses.
{
"method": "tools/call",
"params": {
"name": "enrich_lead",
"arguments": {
"domain": "stripe.com"
}
},
"result": {
"companyName": "Stripe, Inc.",
"industry": "Financial Services",
"headcount": "7000+",
"technographics": ["React", "Ruby on Rails", "AWS", "Salesforce"],
"intentSignals": {
"expansion": "High",
"recentFunding": "Series I",
"hiringTrends": "Increasing in EMEA"
},
"confidenceScore": 0.98
}
}
Risk-Free Metered Billing
Most enrichment APIs charge you for every request, regardless of whether the data is accurate or if the model failed to parse it. Our infrastructure utilizes a Risk-Free Metered Billing structure. You are only billed for successful enrichments that return a Confidence Score > 0.6. If our system returns a low-quality match or no data at all, the query cost is exactly $0, making it the most cost-effective solution for building autonomous SDR swarms and high-volume sales agents.
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