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MCP GitHub Issue Server

by sammcj
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JavaScript
Remote

MCP GitHub 问题服务器

铁匠徽章

铁匠徽章

一个 MCP 服务器，为 LLM 提供使用 GitHub 问题作为待完成任务的能力。该服务器允许 LLM 获取 GitHub 问题详情并将其用作任务描述。

安装

手动安装

npx mcp-github-issue

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 MCP GitHub Issue Server：

npx -y @smithery/cli install mcp-github-issue --client claude

Related MCP server: mcp-github-issues

用法

作为 MCP 服务器

添加到您的 MCP 配置：

{
  "mcpServers": {
    "github-issue": {
      "command": "npx",
      "args": ["mcp-github-issue"]
    }
  }
}

可用工具

获取问题任务

获取 GitHub 问题详细信息以用作任务。

输入模式：

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "GitHub issue URL (https://github.com/owner/repo/issues/number)"
    }
  },
  "required": ["url"]
}

示例用法：

<use_mcp_tool>
<server_name>github-issue</server_name>
<tool_name>get_issue_task</tool_name>
<arguments>
{
  "url": "https://github.com/owner/repo/issues/123"
}
</arguments>
</use_mcp_tool>

响应格式：

{
  "task": {
    "title": "Issue Title",
    "description": "Issue Description/Body",
    "source": "https://github.com/owner/repo/issues/123"
  }
}

特征

  • 从公共存储库获取 GitHub 问题详细信息

  • 公共存储库无需身份验证

  • 返回结构化任务数据，包括标题、描述和源 URL

  • 与模型上下文协议（MCP）兼容

发展

# Install dependencies
npm install

# Build the project
npm run build

# Run the server locally
npm run serve

# Format code
npm run format

# Run MCP inspector
npm run inspector

执照

麻省理工学院

作者

山姆·麦克劳德（ https://smcleod.net ）

Available Tools

1 tool
get_issue_taskC

Fetch GitHub issue details to use as a task

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesGitHub issue URL (https://github.com/owner/repo/issues/number)

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Fetch' which implies a read operation, but doesn't mention any behavioral traits like rate limits, authentication needs, error handling, or what 'as a task' entails. This leaves significant gaps in understanding how the tool behaves.

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?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly.

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

Completeness2/5

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

Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what 'GitHub issue details' includes, how the data is returned, or what 'as a task' means operationally. For a tool with no structured support, more descriptive context is needed to be fully helpful.

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 description coverage is 100%, with the parameter 'url' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as format details or usage examples. With high schema coverage, the baseline score 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 clearly states the action ('Fetch') and resource ('GitHub issue details'), making the purpose understandable. It adds context about using the fetched data 'as a task', which provides additional intent. However, with no sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score.

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 provides no guidance on when to use this tool versus alternatives or any prerequisites. It mentions using the fetched details 'as a task', which hints at a potential use case but doesn't offer explicit when/when-not instructions or context for selection.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedget_issue_task
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TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it against. The tool's purpose is clearly defined as fetching GitHub issue details for task usage.

Naming Consistency5/5

The single tool name 'get_issue_task' follows a consistent verb_noun pattern, using snake_case. Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.

Tool Count2/5

A single tool is too few for a server named 'MCP GitHub Issue Server', as this suggests a broader scope for managing GitHub issues. Typically, such a server would include multiple tools for operations like creating, updating, listing, or closing issues, making this set feel incomplete and thin.

Completeness1/5

The tool set is severely incomplete for the apparent domain of GitHub issue management. It only provides a 'get' operation, lacking essential CRUD/lifecycle coverage such as create, update, delete, list, or search, which will likely cause agent failures in handling issue-related tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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