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GitHub MCP Server

by joewilsonai
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JavaScript
Remote

GitHub MCP 서버

GitHub API와 상호 작용하기 위한 도구를 제공하는 모델 컨텍스트 프로토콜(MCP) 서버입니다. 현재 설명, 주제 및 웹사이트 URL을 사용하여 저장소를 생성할 수 있습니다.

특징

  • 설명에서 자동 생성된 이름으로 GitHub 저장소 만들기

  • 저장소에 주제/태그 추가

  • 저장소 홈페이지 설정

  • README 파일로 저장소 자동 초기화

Related MCP server: GitHub MCP Server

설치

  1. 저장소를 복제합니다

  2. 종속성 설치:

지엑스피1

  1. 서버를 빌드하세요:

npm run build

구성

서버에는 저장소 생성 권한이 있는 GitHub 개인 액세스 토큰이 필요합니다. MCP 설정 파일에 다음을 추가하세요.

{
  "mcpServers": {
    "github": {
      "command": "node",
      "args": ["path/to/github-server/build/index.js"],
      "env": {
        "GITHUB_TOKEN": "your-github-token"
      }
    }
  }
}

사용 가능한 도구

리포지토리 생성

자연어 명령을 사용하여 GitHub 저장소를 만들거나 업데이트합니다.

명령 형식

이 도구는 다양한 작업에 대한 자연어 명령을 허용합니다.

저장소 생성:

Create a repository for [description] with tags [tag1 tag2 tag3] website [url]

또는

Make a new repository called [description] tagged with [tag1, tag2, tag3]

저장소 설명 업데이트:

Update [owner/repo] description to [new description]

또는

Change [repo-name] description as [new description]

저장소 태그 업데이트:

Update [owner/repo] tags to [tag1 tag2 tag3]

또는

Set [repo-name] topics as [tag1, tag2, tag3]

저장소 웹사이트 업데이트:

Update [owner/repo] website to [url]

또는

Set [repo-name] homepage as [url]

사용 예

새로운 저장소 만들기:

const result = await use_mcp_tool({
  server_name: "github",
  tool_name: "create_repo",
  arguments: {
    command: "Create a repository for my machine learning image classifier with tags python tensorflow computer-vision website https://example.com/docs"
  }
});

이렇게 하면:

  1. "my-machine-learning-image-classifier"라는 이름의 저장소를 만듭니다.

  2. 설명을 "내 머신 러닝 이미지 분류기"로 설정하세요.

  3. 저장소 주제로 "python", "tensorflow" 및 "computer-vision"을 추가합니다.

  4. 웹사이트를 " https://example.com/docs "로 설정하세요

  5. README 파일로 초기화

저장소 설명 업데이트:

const result = await use_mcp_tool({
  server_name: "github",
  tool_name: "create_repo",
  arguments: {
    command: "Update username/existing-repo description to Updated ML project for image classification"
  }
});

저장소 태그 업데이트:

const result = await use_mcp_tool({
  server_name: "github",
  tool_name: "create_repo",
  arguments: {
    command: "Update username/existing-repo tags to machine-learning python updated"
  }
});

저장소 웹사이트 업데이트:

const result = await use_mcp_tool({
  server_name: "github",
  tool_name: "create_repo",
  arguments: {
    command: "Update username/existing-repo website to https://example.com/new-docs"
  }
});

이 도구는 다양한 자연어 패턴과 키워드를 이해합니다.

  • 저장소 생성을 위한 Create/make/new

  • 저장소 업데이트를 위한 업데이트/변경/설정/수정

  • 설명 업데이트를 위한 "description to/as"

  • 태그 업데이트를 위한 "태그/주제"

  • 웹사이트 업데이트를 위한 "웹사이트/홈페이지/url to/as"

개발

서버를 수정하거나 확장하려면:

  1. src/index.ts 를 변경하세요

  2. 서버를 다시 빌드하세요:

npm run build

특허

MIT

Available Tools

1 tool
create_repoC

Create or update GitHub repositories using natural language commands

ParametersJSON Schema
NameRequiredDescriptionDefault
commandYesNatural language command like "Create a repository for my machine learning project with tags python tensorflow" or "Update repository-name description to New description with tags updated ml"

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 the tool can 'create or update' repositories, implying mutation, but doesn't address permissions, rate limits, error handling, or what happens on updates (e.g., overwriting). For a mutation tool with zero annotation coverage, this is insufficient.

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 appropriately sized and front-loaded, making it easy to understand 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's complexity (mutation operation with no annotations and no output schema), the description is incomplete. It lacks details on behavioral traits, error conditions, or return values, which are critical for a tool that modifies GitHub repositories. The high schema coverage doesn't compensate for these gaps.

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 'command' fully documented in the schema. The description adds minimal value beyond the schema by reinforcing the natural language aspect but doesn't provide additional syntax, format details, or examples beyond what's already in the schema. This meets the baseline for high coverage.

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 tool's purpose: 'Create or update GitHub repositories using natural language commands.' It specifies the verb (create/update), resource (GitHub repositories), and method (natural language commands). However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a score of 5.

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 minimal guidance on when to use this tool. It mentions 'using natural language commands' but doesn't specify prerequisites, constraints, or when to prefer this over other methods. No explicit alternatives or exclusions are discussed, leaving usage context vague.

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. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedcreate_repo
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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as creating or updating GitHub repositories, making it distinct by default.

Naming Consistency5/5

The single tool name 'create_repo' follows a clear verb_noun pattern, and since there are no other tools to compare it to, consistency is inherently perfect. There are no deviations or mixed conventions to evaluate.

Tool Count2/5

A single tool for a GitHub server is too few for the typical scope, which usually involves multiple operations like listing repos, managing issues, or handling pull requests. This feels thin and incomplete for the domain.

Completeness2/5

The tool surface is severely incomplete for a GitHub server, as it only covers creating or updating repositories. Obvious gaps include retrieving repos, managing issues, pull requests, and other core GitHub functionalities, which will likely cause agent failures.

Maintenance

ActivityInactive
ResponsivenessSyncing

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