发现优秀的 MCP 服务器

Finance MCP Server

一个用 Python 构建的最小化 MCP 服务器，它暴露了两个示例工具：一个用于将公司名称转换为股票代码，另一个用于从雅虎财经获取财务数据。该项目的重点是学习如何构建和运行 MCP 服务器——使用一个简单的财务场景纯粹作为演示用例。

Stealthee MCP

Enables detection and analysis of pre-public product launches through web search, content extraction, AI-powered scoring, and automated alerting. Provides comprehensive tools for surfacing stealth startup signals before they trend publicly.

mcp-google-sheets

谷歌表格 (Gǔgē biǎogé)

OpenAPI MCP Server

允许人工智能使用简单语言来理解复杂的 OpenAPI。

MCP Goose Subagents Server

An MCP server that enables AI clients to delegate tasks to autonomous developer teams using Goose CLI subagents, supporting parallel or sequential execution of specialized agents for different development roles.

ピティナニュースMCPサーバー

PTNA 新闻源的 MCP 服务器

MCP Server

A JSON-RPC 2.0 compliant server that enables interaction with HDF5 data files and Slurm job scheduling through standardized API endpoints.

Aseprite MCP Tools

用于与 Aseprite API 交互的 MCP 服务器

Atlassian Jira MCP Server

用于 Atlassian Jira 的 Node.js/TypeScript MCP 服务器。为 AI 系统（LLM）配备工具，以列出/获取项目、搜索/获取问题（使用 JQL/ID）以及查看开发信息（提交、PR）。将 AI 功能直接连接到 Jira 项目管理和问题跟踪工作流程中。

IACR Cryptology ePrint Archive MCP Server

镜子 (jìng zi)

mcptut1

MCP服务器和客户端教程

MCP Network Sentinel

一个用于 MCP 服务器的网络监控工具，它会记录所有网络活动，以帮助识别潜在的安全问题。

Highrise MCP Server by CData

This read-only MCP Server allows you to connect to Highrise data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

mac-apps-launcher

fork from https://github.com/JoshuaRileyDev/mac-apps-launcher

Omni Server

一个用于熟悉模型上下文协议 (Model Context Protocol) 的 MCP 服务器。 Or, more literally: 一个用于熟悉模型上下文协议的 MCP 服务器 (MCP Server)。

MCP Server for Veryfi Document Processing

具有 Veryfi API 访问权限的模型上下文协议服务器

Weather App

Okay, this is a complex request, but I'll provide a conceptual outline and code snippets to illustrate how you might implement a minimal MCP (Microservice Communication Protocol) server for weather data in Python, along with testing and pre-commit setup. I'll focus on clarity and structure rather than production-readiness. **Conceptual Overview** 1. **MCP Definition (Protocol Buffer):** Define the data structures and service interface using Protocol Buffers. This is the core of MCP. 2. **Server Implementation (Python):** Implement a gRPC server in Python that serves weather data based on the MCP definition. 3. **Client (Example):** A simple client to demonstrate how to request data from the server. 4. **Testing (pytest):** Write unit tests to verify the server's functionality. 5. **Pre-commit Hooks:** Configure pre-commit to run linters (e.g., `flake8`, `black`) and tests before committing code. **1. MCP Definition (weather.proto)** Create a file named `weather.proto`: ```protobuf syntax = "proto3"; package weather; service WeatherService { rpc GetWeather(WeatherRequest) returns (WeatherResponse) {} } message WeatherRequest { string city = 1; } message WeatherResponse { string city = 1; float temperature = 2; string condition = 3; } ``` **Explanation:** * `syntax = "proto3";`: Specifies the Protocol Buffer version. * `package weather;`: Defines the package name for the generated code. * `service WeatherService`: Defines the service with a single RPC method `GetWeather`. * `WeatherRequest`: The request message, containing the city name. * `WeatherResponse`: The response message, containing weather information. **2. Generate gRPC Code (Python)** You'll need the `grpcio-tools` package: ```bash pip install grpcio-tools ``` Then, compile the `.proto` file: ```bash python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. weather.proto ``` This will generate two Python files: `weather_pb2.py` (the data structures) and `weather_pb2_grpc.py` (the gRPC service definitions). **3. Server Implementation (server.py)** ```python import grpc from concurrent import futures import weather_pb2 import weather_pb2_grpc class WeatherServicer(weather_pb2_grpc.WeatherServiceServicer): def GetWeather(self, request, context): city = request.city # Simulate weather data (replace with actual data source) if city == "London": temperature = 15.0 condition = "Cloudy" elif city == "New York": temperature = 25.0 condition = "Sunny" else: temperature = 10.0 condition = "Unknown" return weather_pb2.WeatherResponse(city=city, temperature=temperature, condition=condition) def serve(): server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) weather_pb2_grpc.add_WeatherServiceServicer_to_server(WeatherServicer(), server) server.add_insecure_port('[::]:50051') # Listen on all interfaces, port 50051 server.start() server.wait_for_termination() if __name__ == '__main__': print("Starting gRPC server...") serve() ``` **Explanation:** * `WeatherServicer`: Implements the `WeatherServiceServicer` interface defined in `weather_pb2_grpc.py`. * `GetWeather`: The actual implementation of the RPC method. It receives a `WeatherRequest` and returns a `WeatherResponse`. This example uses hardcoded data for simplicity. In a real application, you'd fetch weather data from an external API or database. * `serve`: Creates a gRPC server, registers the `WeatherServicer`, and starts the server. **4. Client (client.py)** ```python import grpc import weather_pb2 import weather_pb2_grpc def run(): with grpc.insecure_channel('localhost:50051') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) city = "London" request = weather_pb2.WeatherRequest(city=city) response = stub.GetWeather(request) print(f"Weather in {response.city}:") print(f" Temperature: {response.temperature}°C") print(f" Condition: {response.condition}") if __name__ == '__main__': run() ``` **Explanation:** * Creates a gRPC channel to connect to the server. * Creates a `WeatherServiceStub` to make RPC calls. * Sends a `WeatherRequest` for the city "London". * Prints the received `WeatherResponse`. **5. Testing (test_server.py)** Install `pytest` and `grpcio-testing`: ```bash pip install pytest grpcio-testing ``` ```python import pytest import grpc from grpc import aio from concurrent import futures import weather_pb2 import weather_pb2_grpc from server import WeatherServicer # Import your server implementation @pytest.fixture def grpc_server(): server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) weather_pb2_grpc.add_WeatherServiceServicer_to_server(WeatherServicer(), server) port = server.add_insecure_port('[::]:0') # Let the OS choose a port server.start() yield server, port # Provide the server and port to the tests server.stop(0) def test_get_weather(grpc_server): server, port = grpc_server with grpc.insecure_channel(f'localhost:{port}') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) request = weather_pb2.WeatherRequest(city="London") response = stub.GetWeather(request) assert response.city == "London" assert response.temperature == 15.0 assert response.condition == "Cloudy" def test_get_weather_new_york(grpc_server): server, port = grpc_server with grpc.insecure_channel(f'localhost:{port}') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) request = weather_pb2.WeatherRequest(city="New York") response = stub.GetWeather(request) assert response.city == "New York" assert response.temperature == 25.0 assert response.condition == "Sunny" def test_get_weather_unknown(grpc_server): server, port = grpc_server with grpc.insecure_channel(f'localhost:{port}') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) request = weather_pb2.WeatherRequest(city="SomeRandomCity") response = stub.GetWeather(request) assert response.city == "SomeRandomCity" assert response.temperature == 10.0 assert response.condition == "Unknown" ``` **Explanation:** * `grpc_server` fixture: Creates and starts a gRPC server before each test and stops it afterward. It uses a dynamically assigned port to avoid conflicts. * `test_get_weather`: Tests the `GetWeather` method for a specific city ("London"). * `test_get_weather_new_york`: Tests the `GetWeather` method for a specific city ("New York"). * `test_get_weather_unknown`: Tests the `GetWeather` method for an unknown city. Run the tests: ```bash pytest ``` **6. Pre-commit Setup** Install `pre-commit`: ```bash pip install pre-commit ``` Create a `.pre-commit-config.yaml` file in the root of your project: ```yaml repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 # Use the latest stable version hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-added-large-files - repo: https://github.com/psf/black rev: 24.2.0 # Use the latest stable version hooks: - id: black - repo: https://github.com/pycqa/flake8 rev: 7.0.0 # Use the latest stable version hooks: - id: flake8 additional_dependencies: ["flake8-protobuf"] # Add protobuf support - repo: local hooks: - id: pytest name: pytest entry: pytest language: system types: [python] pass_filenames: false ``` **Explanation:** * This configuration uses several popular pre-commit hooks: * `trailing-whitespace`, `end-of-file-fixer`, `check-yaml`, `check-added-large-files`: Basic cleanup and checks. * `black`: Code formatter. * `flake8`: Linter. The `additional_dependencies` line adds support for linting Protocol Buffer-related code. * `pytest`: Runs your tests. Install the pre-commit hooks: ```bash pre-commit install ``` Now, every time you commit, pre-commit will run these checks and formatters. If any of them fail, the commit will be aborted. **Running the Example** 1. **Generate gRPC code:** `python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. weather.proto` 2. **Start the server:** `python server.py` (in a separate terminal) 3. **Run the client:** `python client.py` 4. **Run the tests:** `pytest` 5. **Commit your changes:** `git commit -m "Your commit message"` (pre-commit will run) **Important Considerations and Improvements** * **Error Handling:** The server implementation lacks proper error handling. You should handle exceptions and return appropriate gRPC status codes. * **Data Source:** The server uses hardcoded weather data. Replace this with a real data source (e.g., an API or database). * **Authentication/Authorization:** For production systems, you'll need to add authentication and authorization to secure your gRPC service. * **Logging:** Implement logging for debugging and monitoring. * **Configuration:** Use environment variables or configuration files to manage server settings (e.g., port number, data source connection details). * **Asynchronous gRPC:** Consider using `grpc.aio` for asynchronous gRPC, which can improve performance, especially for I/O-bound operations. * **Health Checks:** Implement a health check endpoint to allow monitoring systems to verify the server's availability. * **Code Generation:** Consider using a build system (e.g., `make`, `nox`) to automate the code generation process. * **Dependency Management:** Use a tool like `poetry` or `pipenv` to manage your project's dependencies. * **CI/CD:** Integrate your tests and pre-commit hooks into a CI/CD pipeline to automate the build, test, and deployment process. This comprehensive example provides a solid foundation for building a gRPC-based microservice with testing and pre-commit setup. Remember to adapt and extend it to meet the specific requirements of your application.

🌱 mcp-origin

管理 MCP 服务器的 MCP 服务器

📸 Smart Photo Journal MCP Server

镜子 (jìng zi)

MCP Developer Server

Provides instant access to 700+ programming documentation sources and creates isolated Docker containers for safe code testing and experimentation. Combines comprehensive documentation lookup with containerized development environments for enhanced development workflows.

Optimizely DXP MCP Server

Enables AI assistants to manage Optimizely DXP deployments through natural language conversations. Supports code deployment, database operations, content synchronization, and environment management across Integration, Preproduction, and Production environments.

mcpservers

MCP Servers 是一个专注于展示和连接模型上下文协议 (Model Context Protocol) 服务器的平台，旨在为开发者提供便捷的 MCP 服务器发现和集成服务。

GPT Image MCP Server

An MCP server that enables text-to-image generation and editing using OpenAI's gpt-image-1 model, supporting multiple output formats, quality settings, and background options.

Amplitude MCP Server

A Model Context Protocol server that enables AI assistants like Claude to track events, page views, user signups, set user properties, and track revenue in Amplitude analytics.

MCP Pokemon Server

An MCP server implementation that enables users to interact with the PokeAPI to fetch Pokemon information through natural language queries.

mcp-server

镜子 (jìng zi)

MCP Jira Server

There are a few ways to interpret "MCP to talk to Jira from cursor," depending on what "MCP" refers to. Here are a few possibilities and their corresponding Chinese translations: **1. If "MCP" refers to a specific tool or plugin called "MCP":** * **English:** Use the MCP tool to interact with Jira from within Cursor. * **Chinese:** 使用 MCP 工具从 Cursor 内部与 Jira 交互。 (Shǐyòng MCP gōngjù cóng Cursor nèibù yǔ Jira jiāohù.) **2. If "MCP" is a typo and you meant "API" (Application Programming Interface):** * **English:** Use an API to interact with Jira from within Cursor. * **Chinese:** 使用 API 从 Cursor 内部与 Jira 交互。 (Shǐyòng API cóng Cursor nèibù yǔ Jira jiāohù.) **3. If "MCP" refers to a general method of communication or a specific protocol (less likely without more context):** * **English:** Use MCP to communicate with Jira from within Cursor. * **Chinese:** 使用 MCP 从 Cursor 内部与 Jira 进行通信。 (Shǐyòng MCP cóng Cursor nèibù yǔ Jira jìnxíng tōngxìn.) **4. If "MCP" is a company or product name (and you want to keep it in English):** * **English:** Use MCP to talk to Jira from cursor. * **Chinese:** 使用 MCP 从 Cursor 内部与 Jira 进行通信。(Shǐyòng MCP cóng Cursor nèibù yǔ Jira jìnxíng tōngxìn.) (Keeping "MCP" in English) **To give you the most accurate translation, please clarify what "MCP" refers to.** For example: * "I'm using a tool called MCP to connect to Jira..." * "I want to use an API to connect to Jira from Cursor..." Once you provide more context, I can give you a more precise and helpful translation.

Spec-driven Development MCP Server

Enables AI-guided spec-driven development workflow that transforms ideas into implementation through structured stages: goal collection, requirements gathering in EARS format, technical design documentation, task planning, and systematic code execution.