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-服务器
该项目实现了一个用于 Mattermost 集成的模型上下文协议 (MCP) 服务器。它连接到 Mattermost API 端点以检索和处理各种信息，并通过标准 MCP 传输提供这些信息。
特征
连接到 Mattermost API 端点
支持多种传输模式：
SSE（服务器发送事件）
标准 I/O
实时消息处理
团队和渠道特定监控
基于令牌的安全身份验证
Related MCP server: Mattermost MCP Server
要求
Node.js >= 22
npm >= 10
dotenvx
设置
克隆此存储库：
git clone https://github.com/kakehashi-inc/mattermost-mcp-server.git
cd mattermost-mcp-server安装依赖项：
npm install设置环境变量：
# Create .env file
cp .env.example .env
# Encrypt your .env file (optional but recommended for production)
dotenvx encrypt所需的环境变量：
MCP_PORT：SSE 传输模式的端口号（默认值：8201）MATTERMOST_ENDPOINT：您的 Mattermost 服务器 URLMATTERMOST_TOKEN：您的 Mattermost 身份验证令牌MATTERMOST_TEAM_ID：要监控的团队的 IDMATTERMOST_CHANNELS：要监控的通道名称的逗号分隔列表
构建服务器：
npm run build用法
该服务器可以以两种传输模式运行：
SSE 传输模式
npm start标准 I/O 传输模式
npm start -- --stdio发展
npm run dev：使用热重载以开发模式启动服务器npm run lint：运行 ESLintnpm run format：使用 Prettier 格式化代码npm test：运行测试npm run inspect：运行 MCP 检查器
参考
执照
麻省理工学院
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
Related MCP Connectors
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
A Model Context Protocol server for Wix AI tools
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
Related MCP Servers
- -licenseNot gradedqualityNot gradedmaintenanceA Model Context Protocol server that enables Claude to interact with Mattermost instances, supporting post management, channel operations, user management, and reaction management.10 npm1-
- FlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables real-time communication using Server-Sent Events (SSE), providing standardized model management and resource templating capabilities.-
- AlicenseNot gradedqualityDmaintenanceA Model Context Protocol (MCP) server that supports STDIO, SSE and Streamable HTTP protocols for AI model interactions.92 npm1MIT