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GitHub MCP Server

by joewilsonai
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JavaScript
Remote

GitHub MCP サーバー

GitHub APIと連携するためのツールを提供するModel Context Protocol（MCP）サーバー。現在、説明、トピック、ウェブサイトURLを含むリポジトリの作成をサポートしています。

特徴

  • 説明から自動生成された名前で GitHub リポジトリを作成する

  • リポジトリにトピック/タグを追加する

  • リポジトリのホームページを設定する

  • README ファイルでリポジトリを自動初期化する

Related MCP server: GitHub MCP Server

インストール

  1. リポジトリをクローンする

  2. 依存関係をインストールします:

npm install

  1. サーバーを構築します。

npm run build

構成

サーバーには、リポジトリ作成権限を持つGitHub個人アクセストークンが必要です。MCP設定ファイルに以下の行を追加してください。

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

利用可能なツール

リポジトリの作成

自然言語コマンドを使用して GitHub リポジトリを作成または更新します。

コマンド形式

このツールは、さまざまな操作のための自然言語コマンドを受け入れます。

リポジトリの作成:

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

または

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

リポジトリの説明を更新しています:

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

または

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

リポジトリタグを更新しています:

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

または

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

リポジトリ Web サイトを更新しています:

Update [owner/repo] website to [url]

または

Set [repo-name] homepage as [url]

使用例

新しいリポジトリの作成:

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"
  }
});

これにより、次のようになります。

  1. 「my-machine-learning-image-classifier」という名前のリポジトリを作成します。

  2. 説明を「私の機械学習画像分類器」に設定します

  3. リポジトリトピックとして「python」、「tensorflow」、「computer-vision」を追加します

  4. ウェブサイトを「 https://example.com/docs 」に設定します

  5. READMEファイルで初期化する

リポジトリの説明を更新しています:

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"
  }
});

リポジトリタグを更新しています:

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"
  }
});

リポジトリ Web サイトを更新しています:

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"
  }
});

このツールは、さまざまな自然言語パターンとキーワードを理解します。

  • リポジトリを作成するための Create/make/new

  • リポジトリを更新するための更新/変更/設定/修正

  • 説明を更新するための「description to/as」

  • タグを更新するための「tags/topics to/as」

  • ウェブサイトを更新するための「ウェブサイト/ホームページ/URL to/as」

発達

サーバーを変更または拡張するには:

  1. src/index.tsに変更を加える

  2. サーバーを再構築します。

npm run build

ライセンス

マサチューセッツ工科大学

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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