GitHub Assistant MCP Server

get_issue

View a specific GitHub issue's full body and details by supplying the owner, repository, and issue number.

Instructions

Get the full details and description of a specific GitHub issue.

Use this when the user wants to read the body/description of an issue — e.g. "what does issue #42 in pallets/flask say?", "show me the details of issue 1500 in django/django".

Args: owner: Repository owner (GitHub username or organisation). repo: Repository name. issue_number: The issue number shown in the GitHub URL after /issues/

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": {
      "owner": "<owner>",
      "repo": "<repo>",
      "issue_number": "<issue_number>"
    },
    "name": "get_issue"
  }
}

Input Schema

Name	Required	Description	Default
`repo`	Yes
`owner`	Yes
`issue_number`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

A4.2/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. The verb 'Get' signals a read-only operation, and the description says it returns details. However, it does not disclose potential edge cases such as 404s, private-repo authentication requirements, or rate limits. This is acceptable for a simple read tool but not richly transparent.

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?

Each sentence earns its place: a front-loaded definition, two concrete trigger examples, and a compact argument list. There is no fluff or redundant restating of 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 operation with an output schema present, the description covers what the tool acts on, when to use it, and what each argument means. A minor gap is not naming list_issues as the alternative for browsing multiple issues, but this does not impede correct invocation.

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 input schema provides no parameter descriptions, so the description must compensate. It defines all three required parameters: owner, repo, and issue_number, including the helpful hint that issue_number appears in the GitHub URL after /issues/. This is sufficient for an agent to supply correctly formed arguments.

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 first sentence names a specific verb ('Get'), a resource ('a specific GitHub issue'), and the scope ('full details and description'). The focus on a single issue clearly distinguishes it from listing tools like list_issues. The examples reinforce the intended object without ambiguity.

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 explicitly states when to use the tool: when the user wants to read the body/description of an issue. It provides two concrete example queries. It does not explicitly contrast with list_issues, but the single-issue framing is clear enough for correct selection.

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

Deploy Server