JMT x402 MCP Server

summarize

Condense lengthy text into concise summaries using a local LLM. Each summary costs $0.05 via x402 USDC payment.

Instructions

AI-powered text summary via local LLM. Price: $0.05/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": {
      "text": "<text>"
    },
    "name": "summarize"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Text to summarize

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 13, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description carries full burden. It mentions AI-powered and local LLM but fails to disclose behaviors like whether the operation is read-only, any side effects, or response format. The pricing detail is useful but insufficient for full transparency.

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 brief and front-loaded with the core purpose. The pricing detail adds relevant context without excessive verbosity, earning a high conciseness score.

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

Completeness2/5

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

The tool has no output schema and no annotations, so the description should clarify return format, length limits, or quality guarantees. It fails to do so, making it incomplete for an agent to fully understand the tool's behavior.

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% for the single parameter 'text', which already describes its role. The description adds no new semantics beyond the schema, so baseline 3 is appropriate.

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?

The description clearly states the tool's function: 'AI-powered text summary via local LLM.' This is specific and distinguishes it from sibling tools like 'analyze' or 'deep_research' which have broader purposes.

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?

The description provides no guidance on when to use this tool versus alternatives such as 'ai_answer' or 'analyze'. It only mentions pricing, which is relevant but not usage context.

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