JMT x402 MCP Server

ai_answer

Answer questions with citations using AI-powered web search and local LLM. Each call costs $0.03 via x402 (USDC on Base).

Instructions

AI-powered search answer with citations. SearXNG + local LLM. Price: $0.03/call via x402 (USDC on Base mainnet).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "q": "<q>"
    },
    "name": "ai_answer"
  }
}

Input Schema

Name	Required	Description	Default
`q`	Yes	Question to answer

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 13, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description adds value by disclosing the cost ($0.03/call via x402) and technical setup (SearXNG + local LLM). However, it does not explain behavioral aspects such as response time, token limits, or potential errors, which would help the agent make informed decisions.

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

Conciseness4/5

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

The description is concise with two sentences that front-load the purpose and key details (technology and cost). It is efficient, though the cost detail could be secondary; still, no waste.

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

Completeness3/5

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

For a single-parameter tool with no output schema, the description covers the main purpose and cost. However, it omits what the answer format looks like (e.g., structure of citations) and does not set expectations for response quality, which is important given siblings like 'news_briefing' and 'summarize'.

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?

The input schema covers 100% of parameters, with 'q' described as 'Question to answer'. The description adds little beyond this, though mentioning 'search answer with citations' implies the question should be fact-seeking. Given high schema coverage, baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool provides an 'AI-powered search answer with citations' using SearXNG and a local LLM, specifying the action and resource. However, it does not explicitly differentiate from sibling tools like 'web_search' or 'summarize', which could lead to ambiguity.

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

Usage Guidelines3/5

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

The description implies usage when a synthesized answer with citations is needed, but lacks explicit guidance on when not to use it or how it compares to alternatives like 'web_search' or 'summarize'. The cost mention is useful but does not address usage conditions.

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