MCP-DOC-MID

get_endpoint_doc

Retrieve full OpenAPI/Swagger documentation for an endpoint, including parameters, request body, and dereferenced responses. Use it to answer API integration questions.

Instructions

Obtiene la documentación completa, parámetros, request body y respuestas dereferenciadas de un endpoint específico.

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": {
      "path": "<path>"
    },
    "name": "get_endpoint_doc"
  }
}

Input Schema

Name	Required	Description
`path`	Yes	Ruta del endpoint (ej. "/v1/members/{memberId}/balances", "/v1/security/login").
`method`	No	Método HTTP (GET, POST, PUT, DELETE, PATCH). Por defecto GET.
`specId`	No	ID o nombre de la especificación a consultar (opcional).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 24, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool performs a read-only retrieval and specifically highlights that responses are 'dereferenciadas' (resolving $refs), which adds useful behavioral detail. However, it does not mention rate limits, authentication, or what a complete response structure actually looks like.

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?

One concise, dense sentence that starts with the verb and quickly enumerates the returned artifacts. No fluff, redundant phrases, or repetitive wording. It is front-loaded with the core action and object.

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?

For a simple read-only tool with just 3 well-documented parameters and no output schema, the description sufficiently states what the tool retrieves and the key behavioral trait of de-referencing. The lack of an explicit when-to-use contrast is a gap, but that falls largely under usage guidance rather than overall completeness for invocation.

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 schema covers all three parameters with detailed descriptions and an enum for method, achieving 100% schema description coverage. The description adds no extra information beyond what the schema already provides, so the baseline of 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 states the specific verb 'Obtiene' and the exact resource: complete documentation including parameters, request body, and de-referenced responses for a specific endpoint. It clearly differentiates from sibling tools like list_specs or get_schema_doc through the 'endpoint específico' phrase, though it does not explicitly name the alternative.

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 gives no guidance on when to use this tool instead of alternatives such as search_docs or get_schema_doc. It merely describes what it retrieves, leaving the selection criteria entirely to the agent to infer. There are no exclusions, prerequisites, or explicit usage contexts.

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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