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alpha-mcp

alpha-mcp

by rsc-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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chatgpt_response_schema

Defines the structure for ChatGPT responses, ensuring consistent output format.

Instructions

The exact key list (and one example) that ChatGPTResponse values must follow.

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": "chatgpt_response_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.8/5.0
Behavior1/5

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

With no annotations, the description carries the full burden of explaining behavior, but it does not state what the tool does (e.g., returns, validates, or enforces the schema). No side effects, permissions, or operational details are disclosed.

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

Conciseness2/5

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

The description is short and to the point, but it is not structured as an actionable tool description. It reads as a passive statement, lacking the imperative or functional clarity expected from a tool definition.

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

Completeness1/5

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

The description does not explain the tool's purpose, its output, or how it fits into the broader workflow. Even though an output schema exists in the context, the description itself is incomplete and unhelpful for an agent deciding to invoke it.

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

Parameters4/5

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

The tool has no parameters, so the schema coverage is trivially complete. The description does not need to explain parameters, and the baseline of 4 applies given zero parameters.

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

Purpose2/5

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

The description states what the tool represents ('the exact key list...that ChatGPTResponse values must follow') but lacks a verb indicating an action. It does not clearly distinguish this tool from its siblings, as it reads more like a specification than a tool behavior.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus any alternative. No conditions, prerequisites, or contexts are mentioned.

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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