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GitHub MCP Server

GitHub MCP Server

list_issues

Lists open issues for a specified GitHub repository, including number, title, author, and URL, to help review bugs and pending tasks.

Instructions

Lista los issues ABIERTOS de un repositorio específico de GitHub (número, título, autor y URL). Úsalo cuando el usuario pregunte qué issues, bugs o tareas pendientes tiene un repositorio.

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": {
      "repo": "<repo>"
    },
    "name": "list_issues"
  }
}

Input Schema

NameRequiredDescriptionDefault
repoYesNombre del repositorio del que se listarán los issues abiertos
ownerNoUsuario u organización dueña del repositorio. Si se omite, se usa el usuario autenticado
perPageNoCantidad máxima de issues a listar (1-100). Por defecto 10

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the burden itself, and it does disclose one meaningful behavioral trait: only open issues are returned, which implies closed ones are filtered out. It says nothing about result ordering, pagination behaviour beyond the schema default, permission requirements or what happens when the repository does not exist.

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?

Two sentences with zero filler: the first states what is returned and the scope, the second states when to invoke it. The scope constraint is front-loaded and nothing is repeated from the schema.

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 three-parameter read tool with fully documented schema, the definition covers purpose, scope and a summary of returned fields, so an agent can call it confidently. The only minor gaps are result ordering and how many issues come back per page, which the schema's perPage default largely covers.

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%, with every parameter, including the owner fallback to the authenticated user and the perPage range, documented directly in the schema. The description only refers to 'un repositorio específico' and adds no syntax, defaults or constraints beyond that, which fits the baseline 3.

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 gives a specific verb and resource ('Lista los issues ABIERTOS') plus the exact scope (a specific GitHub repository) and enumerates the returned fields (número, título, autor y URL). The uppercase 'ABIERTOS' and the listing verb separate it cleanly from siblings such as create_issue, close_issue or add_comment_to_issue.

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?

It states explicitly when to use it: when the user asks what issues, bugs or pending tasks a repository has, which also supplies useful vocabulary for matching user intent. It gives no exclusions or named alternatives, so an agent gets no guidance on what to do for closed issues or filtered searches.

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