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MCP Compras.gov.br

MCP Compras.gov.br

by opedrosoares
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

compras_contrato_responsaveis

Read-onlyIdempotent

Retrieve contract fiscal officers and managers from Compras.gov.br using the contract ID, with masked CPFs for LGPD compliance and pagination support.

Instructions

Lista fiscais/gestores (/api/contrato/{id}/responsaveis).

CPFs mascarados por LGPD (123.***.***-45). Paginação client-side. Cache 15 min.

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": {
      "id_contrato": "<id_contrato>"
    },
    "name": "compras_contrato_responsaveis"
  }
}

Input Schema

NameRequiredDescriptionDefault
paginaNoPágina de resultados (1-based). Padrão 1.
id_contratoYesID do contrato no Comprasnet.
tamanho_paginaNoQuantidade de registros por página. Padrão 50, máximo 500.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.14

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses three meaningful behaviors: LGPD-masked CPFs, client-side pagination, and 15-minute caching. These details materially affect how an agent interprets results and pagination parameters.

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?

Three short sentences deliver the core purpose, the endpoint, and the key behavioral caveats with no filler. Each line adds distinct value and the primary action is front-loaded.

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

Completeness5/5

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

The description covers what the tool lists, which contract it targets, data masking, pagination behavior, and caching. The output schema and annotations supply the remaining safety and return-shape details, so nothing needed for correct invocation is missing.

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 coverage is 100%, so all three parameters are already documented. The description adds a useful note about client-side pagination, which clarifies how 'pagina' and 'tamanho_pagina' behave, but it does not add substantial syntax detail beyond the schema.

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 uses a specific verb ('Lista') and a specific resource ('fiscais/gestores') tied to an explicit endpoint path, so an agent knows exactly what data is returned. It is readily distinguishable from sibling contract tools by naming the responsibility role rather than using a generic label.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool: whenever contract fiscal monitors/managers are needed. It does not name alternative tools or explicitly state when not to use it, but the resource-specific endpoint and role make the use case unambiguous.

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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