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AsyncFuncAI

GitHub Chat MCP

by AsyncFuncAI
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Python
Remote

GitHub チャット MCP

GitHub Chat APIを使用してGitHubリポジトリを分析およびクエリするためのモデルコンテキストプロトコル（MCP）。公式サイト： https://github-chat.com

インストール

# Install with pip
pip install github-chat-mcp

# Or install with the newer uv package manager
uv install github-chat-mcp

  1. クロードと一緒に使い始めましょう！

プロンプトの例:

  • 「github-chat-mcp を使用して React リポジトリを分析する」

  • 「github-chat-mcp で TypeScript リポジトリをインデックスし、そのアーキテクチャについて質問してください」

GitHub チャット MCP サーバー

鍛冶屋のバッジ

Related MCP server: MCP GitHub Reader

セットアップ手順

まず最初に、GitHub Chat APIキーをお持ちであることを確認してください。これはサービスを利用するために必要です。

まずuvをインストールします。

MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

ウィンドウズ:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

カーソルを使ったセットアップ（推奨）

mcp.json の場合:

{
  "mcpServers": {
    "github-chat": {
      "command": "uvx",
      "args": [
        "github-chat-mcp"
      ]
    }
  }
}

上記はフリーミアムリリースなので、envs は必要ありません。

Claude Desktopでのセットアップ

# claude_desktop_config.json
# Can find location through:
# Hamburger Menu -> File -> Settings -> Developer -> Edit Config
# Must perform: brew install uv
{
  "mcpServers": {
    "github-chat": {
      "command": "uvx",
      "args": ["github-chat-mcp"],
      "env": {
      }
    }
  }
}

Smithery経由でインストール

Smithery 経由で Claude Desktop 用の GitHub Chat を自動的にインストールできます。

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

ClaudeとGitHub Chatを使う

  1. まず GitHub リポジトリをインデックスします:「 https://github.com/username/repoで GitHub リポジトリをインデックスします」

  2. 次に、リポジトリについて質問します。「このリポジトリで使用されているコア技術スタックは何ですか？」

デバッグ

走る：

npx @modelcontextprotocol/inspector uvx github-chat-mcp

ローカル/開発環境のセットアップ手順

クローンリポジトリ

git clone https://github.com/yourusername/github-chat-mcp.git

依存関係をインストールする

まずuvをインストールします。

MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

ウィンドウズ:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

次に、MCP サーバーの依存関係をインストールします。

cd github-chat-mcp

# Create virtual environment and activate it
uv venv

source .venv/bin/activate # MacOS/Linux
# OR
.venv/Scripts/activate # Windows

# Install dependencies
uv sync

Claude Desktopでのセットアップ

MCP CLI SDKの使用

# `pip install mcp[cli]` if you haven't
mcp install /ABSOLUTE/PATH/TO/PARENT/FOLDER/github-chat-mcp/src/github_chat_mcp/server.py -v "GITHUB_API_KEY=API_KEY_HERE"

手動で

# claude_desktop_config.json
# Can find location through:
# Hamburger Menu -> File -> Settings -> Developer -> Edit Config
{
  "mcpServers": {
    "github-chat": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/github-chat-mcp",
        "run",
        "github-chat-mcp"
      ],
      "env": {
      }
    }
  }
}

ClaudeとGitHub Chatを使う

  1. まず GitHub リポジトリをインデックスします:「 https://github.com/username/repoで GitHub リポジトリをインデックスします」

  2. 次に、リポジトリについて質問します。「このリポジトリで使用されているコア技術スタックは何ですか？」

デバッグ

走る：

# If mcp cli installed (`pip install mcp[cli]`)
mcp dev /ABSOLUTE/PATH/TO/PARENT/FOLDER/github-chat-mcp/src/github_chat_mcp/server.py

# If not
npx @modelcontextprotocol/inspector \
      uv \
      --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/github-chat-mcp \
      run \
      github-chat-mcp

次に、MCP Inspector http://localhost:5173にアクセスします。インスペクターの環境変数GITHUB_API_KEYにGitHub APIキーを追加する必要があるかもしれません。

注記

  • ログレベルは、 FASTMCP_LOG_LEVEL環境変数を通じて調整可能です（例： FASTMCP_LOG_LEVEL="ERROR" ）

  • この MCP サーバーは、主に 2 つのツールを提供します。

    1. リポジトリのインデックス作成 - GitHub リポジトリのインデックス作成と分析

    2. リポジトリクエリ - インデックスされたリポジトリについて質問する

Available Tools

2 tools
index_repositoryA

Index a GitHub repository to analyze its codebase. This must be done before asking questions about the repository.

ParametersJSON Schema
NameRequiredDescriptionDefault
repo_urlYesThe GitHub repository URL to index (format: https://github.com/username/repo).

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 carries full burden. It discloses that indexing is required before querying (a behavioral constraint) but doesn't mention other traits like whether indexing is idempotent, how long it takes, error conditions, or what 'analyze its codebase' entails operationally. The description adds some context but leaves significant behavioral aspects unspecified.

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 waste: the first states the purpose, the second provides crucial usage guidance. Every word earns its place, and the most important information (the prerequisite nature) is front-loaded in the second sentence.

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?

Given the tool's complexity (a mutation operation with no annotations and no output schema), the description is reasonably complete for its core purpose and workflow context. It explains why indexing is needed and how it relates to querying, though it could better address behavioral aspects like what 'indexing' actually does or what happens on repeated calls.

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 description coverage is 100% (the single parameter 'repo_url' is fully documented in the schema with format details). The description doesn't add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.

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 clearly states the specific action ('index') and resource ('GitHub repository') with the purpose 'to analyze its codebase'. It distinguishes from the sibling tool 'query_repository' by explaining this is a prerequisite step before querying.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool ('before asking questions about the repository') and implies an alternative workflow with the sibling tool 'query_repository'. Provides clear context about the prerequisite nature of indexing.

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

query_repositoryB

Ask questions about a GitHub repository and receive detailed AI responses. The repository must be indexed first.

ParametersJSON Schema
NameRequiredDescriptionDefault
repo_urlYesThe GitHub repository URL to query (format: https://github.com/username/repo).
questionYesThe question to ask about the repository.
conversation_historyNoPrevious conversation history for multi-turn conversations.

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the indexing prerequisite but doesn't describe other important behaviors: what types of questions are supported, whether there are rate limits, authentication requirements, response format, or error conditions. For a tool with AI responses and conversation history, this leaves significant gaps.

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 extremely concise with just two sentences that directly state the tool's purpose and key prerequisite. Every word earns its place, and the information is front-loaded with no unnecessary elaboration or repetition.

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?

For a tool that queries repositories with AI responses and supports conversation history, the description is incomplete. With no annotations and no output schema, the description doesn't explain what the AI responses contain, how conversation history should be structured, error handling, or limitations. The indexing prerequisite is mentioned, but other critical context is missing.

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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'questions about a GitHub repository' which aligns with the parameters but doesn't provide additional semantic context about how parameters interact or special considerations.

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: 'Ask questions about a GitHub repository and receive detailed AI responses.' It specifies the verb ('ask questions'), resource ('GitHub repository'), and outcome ('detailed AI responses'). However, it doesn't explicitly differentiate from its sibling tool 'index_repository' beyond mentioning indexing as a prerequisite.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context by stating 'The repository must be indexed first,' which implies a prerequisite relationship with 'index_repository.' However, it doesn't explicitly state when to use this tool versus alternatives or provide clear exclusions. The guidance is implied rather than explicit.

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. 2 tool updates
    • First observedindex_repository
    • First observedquery_repository
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TDQS

A3.5/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: index_repository is for preparing the repository for analysis, while query_repository is for asking questions about the indexed repository. There is no overlap or ambiguity between them.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (index_repository and query_repository), using the same naming convention and structure throughout the set.

Tool Count2/5

With only 2 tools, the server feels too thin for its apparent purpose of GitHub repository analysis. It lacks essential operations like listing repositories, managing indexes, or handling errors, which limits functionality and could cause agent failures.

Completeness2/5

The tool surface is severely incomplete for GitHub repository analysis. It covers only indexing and querying, missing core operations such as repository discovery, index management, or error handling, leading to significant gaps in workflow coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

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