FastMCP

FastMCP——模型上下文协议服务器

使用 FastMCP 实现的轻量级模型上下文协议 (MCP) 服务器， FastMCP是一个用于构建 MCP 服务器和客户端的快速 Pythonic 框架。

特征

创建、检索、更新和删除模型上下文
针对特定上下文执行查询
按模型名称和标签过滤
内存存储（用于开发）
FastMCP 集成，轻松实现 MCP 服务器开发
用于指标和监控的 Datadog 集成

要求

Python 3.7+
FastMCP
uv（推荐用于环境管理）
Datadog 帐户（可选，用于指标）

安装

使用 uv（推荐）

最简单的安装方法是使用提供的脚本：

Unix/Linux/macOS

# Clone the repository
git clone https://github.com/yourusername/datadog-mcp-server.git
cd datadog-mcp-server

# Make the install script executable
chmod +x install.sh

# Run the installer
./install.sh

视窗

# Clone the repository
git clone https://github.com/yourusername/datadog-mcp-server.git
cd datadog-mcp-server

# Run the installer
.\install.ps1

手动安装

# Clone the repository
git clone https://github.com/yourusername/datadog-mcp-server.git
cd datadog-mcp-server

# Create and activate a virtual environment with uv
uv venv
# On Unix/Linux/macOS:
source .venv/bin/activate
# On Windows:
.\.venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

Datadog 配置

该服务器与 Datadog 集成，用于指标和监控。您可以通过多种方式配置 Datadog API 凭据：

1.环境变量

启动服务器之前设置这些环境变量：

# Unix/Linux/macOS
export DATADOG_API_KEY=your_api_key
export DATADOG_APP_KEY=your_app_key  # Optional
export DATADOG_SITE=datadoghq.com    # Optional, default: datadoghq.com

# Windows PowerShell
$env:DATADOG_API_KEY = 'your_api_key'
$env:DATADOG_APP_KEY = 'your_app_key'  # Optional
$env:DATADOG_SITE = 'datadoghq.com'    # Optional

2. .env 文件

在项目目录中创建.env文件：

DATADOG_API_KEY=your_api_key
DATADOG_APP_KEY=your_app_key
DATADOG_SITE=datadoghq.com

3. FastMCP CLI 安装

当作为 Claude Desktop 工具安装时，您可以传递环境变量：

fastmcp install mcp_server.py --name "Model Context Server" -v DATADOG_API_KEY=your_api_key

4.运行时配置

在运行时使用configure_datadog工具：

result = await client.call_tool("configure_datadog", {
    "api_key": "your_api_key",
    "app_key": "your_app_key",  # Optional
    "site": "datadoghq.com"     # Optional
})

用法

启动服务器

# Start directly from activated environment
python mcp_server.py

# Or use uv run (no activation needed)
uv run python mcp_server.py

# Use FastMCP CLI for development (if in activated environment)
fastmcp dev mcp_server.py

# Use FastMCP CLI with uv (no activation needed)
uv run -m fastmcp dev mcp_server.py

安装为 Claude 桌面工具

# From activated environment
fastmcp install mcp_server.py --name "Model Context Server"

# Using uv directly
uv run python -m fastmcp install mcp_server.py --name "Model Context Server"

# With Datadog API key
fastmcp install mcp_server.py --name "Model Context Server" -v DATADOG_API_KEY=your_api_key

使用工具

该服务器提供以下工具：

create_context创建一个新的上下文
get_context - 检索特定上下文
update_context - 更新现有上下文
delete_context - 删除上下文
list_contexts - 列出所有上下文（带有可选过滤）
query_model - 针对特定上下文执行查询
health_check - 服务器健康检查
configure_datadog - 在运行时配置 Datadog 集成

示例请求

创建上下文

result = await client.call_tool("create_context", {
    "context_id": "model-123",
    "model_name": "gpt-3.5",
    "data": {
        "parameters": {
            "temperature": 0.7
        }
    },
    "tags": ["production", "nlp"]
})

执行查询

result = await client.call_tool("query_model", {
    "context_id": "model-123",
    "query_data": {
        "prompt": "Hello, world!"
    }
})

配置 Datadog

result = await client.call_tool("configure_datadog", {
    "api_key": "your_datadog_api_key",
    "app_key": "your_datadog_app_key",  # Optional
    "site": "datadoghq.com"             # Optional
})

Datadog 指标

服务器向 Datadog 报告以下指标：

mcp.contexts.created - 上下文创建事件
mcp.contexts.updated - 上下文更新事件
mcp.contexts.deleted - 上下文删除事件
mcp.contexts.accessed - 上下文访问事件
mcp.contexts.total - 上下文总数
mcp.contexts.listed - 列出上下文操作事件
mcp.queries.executed - 查询执行事件
mcp.server.startup - 服务器启动事件
mcp.server.shutdown - 服务器关闭事件

发展

请参阅包含的mcp_example.py以获取客户端实现示例：

# Run the example client (with activated environment)
python mcp_example.py

# Run with uv (no activation needed)
uv run python mcp_example.py

执照

麻省理工学院

资源

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

轻量级模型上下文协议服务器，支持通过集成的 Datadog 指标和监控来创建、管理和查询模型上下文。

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