A
licenseNot graded
qualityD
maintenanceA server that implements the Model Context Protocol, providing a standardized way to connect AI models to different data sources and tools.
8 npm
11
MIT
Mattermost MCP Server
mattermost-mcp-server
このプロジェクトは、Mattermost統合のためのモデルコンテキストプロトコル（MCP）サーバーを実装します。Mattermost APIエンドポイントに接続して様々な情報を取得・処理し、標準のMCPトランスポートを介して利用できるようにします。
特徴
Mattermost APIエンドポイントに接続します
複数のトランスポート モードをサポートします。
SSE (サーバー送信イベント)
標準I/O
リアルタイムメッセージ処理
チームおよびチャネル固有の監視
安全なトークンベースの認証
Related MCP server: Mattermost MCP Server
要件
Node.js >= 22
npm >= 10
ドテンvx
設定
このリポジトリをクローンします:
git clone https://github.com/kakehashi-inc/mattermost-mcp-server.git
cd mattermost-mcp-server依存関係をインストールします:
npm install環境変数を設定します。
# Create .env file
cp .env.example .env
# Encrypt your .env file (optional but recommended for production)
dotenvx encrypt必要な環境変数:
MCP_PORT: SSEトランスポートモードのポート番号（デフォルト: 8201）MATTERMOST_ENDPOINT: MattermostサーバーのURLMATTERMOST_TOKEN: Mattermost認証トークンMATTERMOST_TEAM_ID: 監視するチームのIDMATTERMOST_CHANNELS: 監視するチャンネル名のカンマ区切りリスト
サーバーを構築します。
npm run build使用法
サーバーは 2 つのトランスポート モードで実行できます。
SSEトランスポートモード
npm start標準I/Oトランスポートモード
npm start -- --stdio発達
npm run dev: ホットリロードで開発モードでサーバーを起動するnpm run lint: ESLint を実行するnpm run format: Prettier を使ってコードをフォーマットするnpm test: テストを実行するnpm run inspect: MCPインスペクターを実行する
参考文献
ライセンス
マサチューセッツ工科大学
Available Tools
1 toolmattermost_searchC
Fetch messages from Mattermost channels with optional search functionality
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| channels | No | List of channel IDs to fetch messages from. If not provided, uses the default channels. | |
| limit | No | Maximum number of messages to fetch per channel. If not provided, uses the default limit. | |
| query | No | Search query to filter messages. If provided, performs a search instead of fetching recent messages. |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic operation. It doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what 'default channels/limit' entail, which are critical for a fetch/search tool.
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 front-loads the core purpose ('fetch messages') and adds key detail ('optional search functionality'). It's appropriately sized with zero wasted words, earning its place clearly.
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 with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavior, output format, error cases, or how parameters interact (e.g., search overriding fetch), leaving significant gaps for agent understanding.
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 parameters are well-documented in the schema. The description adds minimal value by implying the tool can fetch recent messages or search, but doesn't elaborate on parameter interactions or semantics beyond what the schema provides.
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 messages') and resource ('from Mattermost channels'), with the added detail of 'optional search functionality'. It's specific about what the tool does, though without sibling tools to differentiate from, it can't achieve a perfect 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 mentions 'optional search functionality' but provides no explicit guidance on when to use search vs. fetching recent messages, nor any prerequisites or alternatives. Without siblings, it lacks comparative context, leaving usage unclear beyond the basic function.
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 tool update
v1.0.0- First observed
mattermost_search
TDQS
B3.1/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as fetching messages with optional search, leaving no room for confusion or misselection.
Naming Consistency5/5
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'mattermost_search' follows a clear and descriptive pattern, but consistency cannot be assessed across a set of one.
Tool Count2/5
A single tool is too few for a server named 'Mattermost MCP Server', which implies broader functionality for interacting with Mattermost (e.g., posting messages, managing channels, users). This minimal toolset feels thin and incomplete for the apparent scope.
Completeness2/5
The tool surface is severely incomplete for a Mattermost integration. While 'mattermost_search' covers fetching messages, there are obvious gaps such as creating messages, managing channels, handling users, or other core Mattermost operations, which will likely cause agent failures in typical workflows.
Maintenance
ActivityInactive
ResponsivenessNo issues
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