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Local Maps B2B Lead Extractor MCP

by io.github.ralf9090
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llm_chat_completions

Read-onlyIdempotent

OpenAI-compatible LLM Chat Completions Gateway ($0.002 USDC/call). Drop-in replacement for /v1/chat/completions settled in Base USDC via x402 with zero account or API key required.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "llm_chat_completions"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoModel alias (default: 'gpt-4o-mini')
promptNoUser prompt or question for the LLM
messagesNoOptional OpenAI-format messages array [{role: 'user', content: '...'}]
x_api_keyNoOptional ak_live_... 30-day Prepaid API Pass key to unlock the full unmasked dataset
payment_signatureNoOptional x402 v2/v1 Base64 or JSON EIP-3009 USDC payment signature to unlock the full unmasked dataset directly via MCP

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive). The description adds genuinely useful behavior beyond them: per-call pricing ($0.002 USDC), settlement via x402 on Base, and no account or API key required. It leaves out latency/rate-limit behavior, but the payment and auth traits are the important disclosures here.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tightly packed sentences with the core purpose front-loaded and payment/access details following. No filler; every clause carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description could have clarified the returned completion format, which it omits. However, it covers purpose, cost, auth, and the payment mechanism adequately, and safety is carried by annotations, so an agent has enough to invoke it correctly. The odd 'unmasked dataset' phrasing in the schema slightly muddies intent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents model, prompt, messages, x_api_key, and payment_signature. The description's mention of zero-account access and x402 settlement lightly reinforces the optional auth params but adds no syntax or format detail beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and framing: an OpenAI-compatible LLM chat completions gateway, explicitly a drop-in replacement for /v1/chat/completions. An agent can immediately tell this generates model completions and is distinct from the data-intel siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not, or alternative selection guidance. It implies a chat-call scenario but never routes the agent against send-a-prompt vs. some other tool, and gives no preconditions beyond the payment rail.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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