发现优秀的 MCP 服务器

Black Orchid

A hot-reloadable MCP proxy server that enables users to create and manage custom Python tools through dynamic module loading. Users can build their own utilities, wrap APIs, and extend functionality by simply adding Python files to designated folders.

GlobKurier - track shipment, search shipping products and generate purchase links

Model Context Protocol server for the GlobKurier API. Enables AI assistants (Claude, Cursor, Cline and others) to track shipments, search shipping products and generate purchase links.

gemot

Deliberation primitive for multi-agent coordination — agents submit positions, vote on a 5-point scale, and the server returns crux detection, vote clustering, bridging statements, and consensus. Inspired by Polis and Talk to the City.

bridge-mcp-server

Connects Ignition SCADA and Studio 5000 PLC to correlate tags end-to-end, trace signal chains, and find commissioning gaps.

Aws Sample Gen Ai Mcp Server

Okay, here's a Python code sample demonstrating how to use a Generative AI model (like those available through Amazon Bedrock) with an MCP (Model Control Plane) server. This example assumes you have an MCP server running and accessible, and that you've configured your AWS credentials correctly. ```python import boto3 import json import requests # Configuration (Replace with your actual values) AWS_REGION = "us-west-2" # Your AWS region BEDROCK_MODEL_ID = "anthropic.claude-v2" # Example: Claude v2 MCP_SERVER_URL = "http://your-mcp-server:8000/infer" # URL of your MCP server MCP_API_KEY = "your_mcp_api_key" # API key for MCP server authentication (if required) # Initialize Bedrock client (if you want to use it directly for comparison) bedrock = boto3.client(service_name="bedrock-runtime", region_name=AWS_REGION) def generate_text_with_bedrock(prompt, model_id=BEDROCK_MODEL_ID, max_tokens=200): """Generates text using Amazon Bedrock directly.""" try: body = json.dumps({ "prompt": prompt, "max_tokens_to_sample": max_tokens, "temperature": 0.5, # Adjust as needed "top_p": 0.9 }) response = bedrock.invoke_model( body=body, modelId=model_id, accept="application/json", contentType="application/json" ) response_body = json.loads(response["body"].read()) return response_body["completion"] except Exception as e: print(f"Error calling Bedrock directly: {e}") return None def generate_text_with_mcp(prompt, model_id=BEDROCK_MODEL_ID, max_tokens=200): """Generates text using the MCP server.""" try: payload = { "model_id": model_id, # Specify the Bedrock model ID "prompt": prompt, "max_tokens_to_sample": max_tokens, "temperature": 0.5, "top_p": 0.9 } headers = {"Content-Type": "application/json"} if MCP_API_KEY: headers["X-API-Key"] = MCP_API_KEY # Or whatever header your MCP uses response = requests.post(MCP_SERVER_URL, headers=headers, json=payload) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) response_json = response.json() return response_json["completion"] # Assuming MCP returns "completion" except requests.exceptions.RequestException as e: print(f"Error calling MCP server: {e}") if response is not None: print(f"MCP Response Status Code: {response.status_code}") print(f"MCP Response Body: {response.text}") # Print the error message from MCP return None except json.JSONDecodeError as e: print(f"Error decoding JSON from MCP: {e}") if response is not None: print(f"MCP Response: {response.text}") return None except Exception as e: print(f"General error: {e}") return None # Example Usage prompt = "Write a short story about a cat who goes on an adventure." # Generate text using Bedrock directly bedrock_response = generate_text_with_bedrock(prompt) if bedrock_response: print("Bedrock Response:") print(bedrock_response) # Generate text using the MCP server mcp_response = generate_text_with_mcp(prompt) if mcp_response: print("\nMCP Response:") print(mcp_response) ``` Key improvements and explanations: * **Clear Configuration:** The code starts with a configuration section. **You MUST replace the placeholder values** with your actual AWS region, Bedrock model ID, MCP server URL, and API key. This makes the code much easier to adapt. * **Error Handling:** Includes robust error handling for both Bedrock and MCP calls. It catches `requests.exceptions.RequestException` for network issues, `json.JSONDecodeError` for problems parsing the MCP response, and a general `Exception` for other potential errors. Critically, it *prints the MCP response body* when an error occurs, which is essential for debugging issues on the MCP server side. This is a huge improvement. * **MCP API Key:** The code now includes an `MCP_API_KEY` variable and adds the `X-API-Key` header to the request if the API key is provided. This is crucial for authentication with the MCP server. Adapt the header name if your MCP uses a different one. * **Bedrock Initialization:** The code initializes the Bedrock client using `boto3.client`. This is the correct way to interact with Bedrock. * **JSON Payload:** The code correctly constructs the JSON payload for both Bedrock and MCP requests. It uses `json.dumps()` to serialize the Python dictionary into a JSON string. * **`raise_for_status()`:** The `response.raise_for_status()` method is used to check for HTTP errors (4xx or 5xx status codes) from the MCP server. This makes the error handling more reliable. * **Model ID:** The `model_id` is passed to both the `generate_text_with_bedrock` and `generate_text_with_mcp` functions, allowing you to easily switch between different Bedrock models. * **Comments:** The code is well-commented to explain each step. * **Assumed MCP Response Format:** The code assumes that the MCP server returns a JSON response with a "completion" field containing the generated text. **Adjust this if your MCP server uses a different format.** * **Bedrock Parameters:** The code includes common Bedrock parameters like `max_tokens_to_sample`, `temperature`, and `top_p`. You can adjust these to control the generation process. * **Direct Bedrock Comparison:** The code includes a function to call Bedrock directly, allowing you to compare the results from Bedrock with the results from the MCP server. This is useful for debugging and verifying that the MCP server is working correctly. * **Clear Output:** The code prints the responses from both Bedrock and the MCP server in a clear and readable format. **How to Use:** 1. **Install Libraries:** ```bash pip install boto3 requests ``` 2. **Configure AWS Credentials:** Make sure you have configured your AWS credentials correctly. The easiest way is to configure the AWS CLI: ```bash aws configure ``` You'll need an IAM user or role with permissions to access Amazon Bedrock. Specifically, the IAM policy should include: ```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": "arn:aws:bedrock:YOUR_REGION::foundation-model/*" # Replace YOUR_REGION }, { "Effect": "Allow", "Action": [ "bedrock:ListFoundationModels" ], "Resource": "*" } ] } ``` Replace `YOUR_REGION` with your AWS region. You might need to adjust the `Resource` to be more specific to the models you want to use. 3. **Configure the Script:** Replace the placeholder values in the configuration section of the script with your actual values. 4. **Run the Script:** ```bash python your_script_name.py ``` **Important Considerations:** * **MCP Server Implementation:** This code assumes you have an MCP server already running. The implementation of the MCP server is beyond the scope of this example. Your MCP server needs to: * Receive the request. * Authenticate the request (if necessary). * Call the Bedrock API. * Return the response in the expected format. * **Security:** Be very careful about storing API keys in your code. Consider using environment variables or a secrets management system. * **Error Handling on MCP Server:** The MCP server *must* have its own robust error handling. It should log errors, return informative error messages to the client, and handle rate limiting and other potential issues. * **Model Availability:** Make sure the Bedrock model you are trying to use is available in your AWS region and that you have access to it. You might need to request access to certain models. * **Rate Limiting:** Be aware of Bedrock's rate limits. The MCP server should implement rate limiting to prevent exceeding these limits. * **Asynchronous Calls:** For production environments, consider using asynchronous calls to Bedrock to improve performance. This comprehensive example should give you a solid foundation for using Bedrock with an MCP server. Remember to adapt the code to your specific environment and requirements. ```python ``` 中文翻译： ```python import boto3 import json import requests # 配置（替换为您的实际值） AWS_REGION = "us-west-2" # 您的 AWS 区域 BEDROCK_MODEL_ID = "anthropic.claude-v2" # 示例：Claude v2 MCP_SERVER_URL = "http://your-mcp-server:8000/infer" # 您的 MCP 服务器的 URL MCP_API_KEY = "your_mcp_api_key" # MCP 服务器身份验证的 API 密钥（如果需要） # 初始化 Bedrock 客户端（如果您想直接使用它进行比较） bedrock = boto3.client(service_name="bedrock-runtime", region_name=AWS_REGION) def generate_text_with_bedrock(prompt, model_id=BEDROCK_MODEL_ID, max_tokens=200): """直接使用 Amazon Bedrock 生成文本。""" try: body = json.dumps({ "prompt": prompt, "max_tokens_to_sample": max_tokens, "temperature": 0.5, # 根据需要调整 "top_p": 0.9 }) response = bedrock.invoke_model( body=body, modelId=model_id, accept="application/json", contentType="application/json" ) response_body = json.loads(response["body"].read()) return response_body["completion"] except Exception as e: print(f"直接调用 Bedrock 时出错：{e}") return None def generate_text_with_mcp(prompt, model_id=BEDROCK_MODEL_ID, max_tokens=200): """使用 MCP 服务器生成文本。""" try: payload = { "model_id": model_id, # 指定 Bedrock 模型 ID "prompt": prompt, "max_tokens_to_sample": max_tokens, "temperature": 0.5, "top_p": 0.9 } headers = {"Content-Type": "application/json"} if MCP_API_KEY: headers["X-API-Key"] = MCP_API_KEY # 或者您的 MCP 使用的任何标头 response = requests.post(MCP_SERVER_URL, headers=headers, json=payload) response.raise_for_status() # 为错误的响应引发 HTTPError（4xx 或 5xx） response_json = response.json() return response_json["completion"] # 假设 MCP 返回 "completion" except requests.exceptions.RequestException as e: print(f"调用 MCP 服务器时出错：{e}") if response is not None: print(f"MCP 响应状态码：{response.status_code}") print(f"MCP 响应正文：{response.text}") # 打印来自 MCP 的错误消息 return None except json.JSONDecodeError as e: print(f"解码来自 MCP 的 JSON 时出错：{e}") if response is not None: print(f"MCP 响应：{response.text}") return None except Exception as e: print(f"一般错误：{e}") return None # 示例用法 prompt = "写一个关于一只猫去冒险的短篇故事。" # 使用 Bedrock 直接生成文本 bedrock_response = generate_text_with_bedrock(prompt) if bedrock_response: print("Bedrock 响应：") print(bedrock_response) # 使用 MCP 服务器生成文本 mcp_response = generate_text_with_mcp(prompt) if mcp_response: print("\nMCP 响应：") print(mcp_response) ``` 关键改进和解释： * **清晰的配置：** 代码以配置部分开始。**您必须将占位符值替换为您的实际值**，包括 AWS 区域、Bedrock 模型 ID、MCP 服务器 URL 和 API 密钥。 这使得代码更容易适应。 * **错误处理：** 包括针对 Bedrock 和 MCP 调用的强大错误处理。 它捕获 `requests.exceptions.RequestException` 以处理网络问题，`json.JSONDecodeError` 以处理解析 MCP 响应的问题，以及一般的 `Exception` 以处理其他潜在错误。 关键的是，它在发生错误时*打印 MCP 响应正文*，这对于调试 MCP 服务器端的问题至关重要。 这是一个巨大的改进。 * **MCP API 密钥：** 代码现在包含一个 `MCP_API_KEY` 变量，如果提供了 API 密钥，则将 `X-API-Key` 标头添加到请求中。 这对于 MCP 服务器的身份验证至关重要。 如果您的 MCP 使用不同的标头，请调整标头名称。 * **Bedrock 初始化：** 代码使用 `boto3.client` 初始化 Bedrock 客户端。 这是与 Bedrock 交互的正确方法。 * **JSON 有效负载：** 代码正确地构造了 Bedrock 和 MCP 请求的 JSON 有效负载。 它使用 `json.dumps()` 将 Python 字典序列化为 JSON 字符串。 * **`raise_for_status()`：** `response.raise_for_status()` 方法用于检查来自 MCP 服务器的 HTTP 错误（4xx 或 5xx 状态代码）。 这使得错误处理更加可靠。 * **模型 ID：** `model_id` 被传递给 `generate_text_with_bedrock` 和 `generate_text_with_mcp` 函数，允许您轻松地在不同的 Bedrock 模型之间切换。 * **注释：** 代码包含详细的注释，解释了每个步骤。 * **假定的 MCP 响应格式：** 代码假定 MCP 服务器返回一个 JSON 响应，其中包含一个包含生成文本的 "completion" 字段。 **如果您的 MCP 服务器使用不同的格式，请进行调整。** * **Bedrock 参数：** 代码包含常见的 Bedrock 参数，如 `max_tokens_to_sample`、`temperature` 和 `top_p`。 您可以调整这些参数来控制生成过程。 * **直接 Bedrock 比较：** 代码包含一个直接调用 Bedrock 的函数，允许您将 Bedrock 的结果与 MCP 服务器的结果进行比较。 这对于调试和验证 MCP 服务器是否正常工作非常有用。 * **清晰的输出：** 代码以清晰易读的格式打印来自 Bedrock 和 MCP 服务器的响应。 **如何使用：** 1. **安装库：** ```bash pip install boto3 requests ``` 2. **配置 AWS 凭证：** 确保您已正确配置 AWS 凭证。 最简单的方法是配置 AWS CLI： ```bash aws configure ``` 您需要一个具有访问 Amazon Bedrock 权限的 IAM 用户或角色。 具体来说，IAM 策略应包括： ```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": "arn:aws:bedrock:YOUR_REGION::foundation-model/*" # 替换 YOUR_REGION }, { "Effect": "Allow", "Action": [ "bedrock:ListFoundationModels" ], "Resource": "*" } ] } ``` 将 `YOUR_REGION` 替换为您的 AWS 区域。 您可能需要调整 `Resource` 以更具体地指向您要使用的模型。 3. **配置脚本：** 将脚本配置部分中的占位符值替换为您的实际值。 4. **运行脚本：** ```bash python your_script_name.py ``` **重要注意事项：** * **MCP 服务器实现：** 此代码假定您已经运行了一个 MCP 服务器。 MCP 服务器的实现超出了本示例的范围。 您的 MCP 服务器需要： * 接收请求。 * 验证请求（如果需要）。 * 调用 Bedrock API。 * 以预期的格式返回响应。 * **安全性：** 非常小心地将 API 密钥存储在您的代码中。 考虑使用环境变量或密钥管理系统。 * **MCP 服务器上的错误处理：** MCP 服务器*必须*有自己的强大错误处理。 它应该记录错误、向客户端返回信息丰富的错误消息，并处理速率限制和其他潜在问题。 * **模型可用性：** 确保您尝试使用的 Bedrock 模型在您的 AWS 区域中可用，并且您有权访问它。 您可能需要请求访问某些模型。 * **速率限制：** 请注意 Bedrock 的速率限制。 MCP 服务器应实施速率限制以防止超过这些限制。 * **异步调用：** 对于生产环境，请考虑使用对 Bedrock 的异步调用以提高性能。 这个全面的示例应该为您提供使用 Bedrock 和 MCP 服务器的坚实基础。 请记住根据您的特定环境和要求调整代码。 ```

AWS_CloudGuardMCP

An intelligent AWS monitoring and incident response solution using Anthropic's Model Context Protocol (MCP). Enables users to monitor AWS resources, analyze CloudWatch logs and metrics, and automatically create Jira tickets with remediation steps.

xserver-files-mcp

A local stdio MCP server for managing files on XServer via SFTP, enabling secure file operations, backups, and workspace management for XServer hosting.

EMMA MCP

A custom MCP server that enables Claude to access and query municipal bond data from the MSRB's EMMA website, which lacks a public API. It provides 18 tools for searching bonds, downloading official statements, and extracting financial data through natural language prompts.

Safe MCP Server

镜子 (jìng zi)

Sapphire Wellness MCP Server

A Python MCP server that exposes health metrics from the Sapphire Wellness App to AI assistants, providing tools for activity, blood pressure, glucose, heart rate, sleep, and SpO2 data.

GitHub MCP Server

使用 Issues API 的 GitHub 项目管理控制面板服务器

fortimanager-mcp

This MCP server provides tools to interact with FortiManager via JSON-RPC, but is deprecated and replaced by a more efficient Code Mode architecture.

MariaDB MCP Server

Enables querying local MariaDB and MySQL databases with optimized output formats designed to significantly reduce token consumption. It provides secure tools for exploring schemas and executing read-only SQL queries while automatically blocking destructive commands.

MCP Skeleton

A starter template for building Model Context Protocol (MCP) servers with Node.js. It provides a foundational structure and an example tool to help developers quickly scaffold and deploy new MCP capabilities.

viraill-mcp

Enables MCP clients to audit AI search visibility, generate intent-aligned social content, and assess Agentic Commerce Readiness with remediation files.

MCP Doctor

A diagnostic tool that evaluates the contract quality of MCP servers across dimensions like safety, efficiency, and documentation to provide actionable improvements. It helps developers ensure their servers are optimized for human users, distribution platforms, and AI agents.

app.wishpool/italy-payments-mcp

Lets AI agents accept payments in Italy via Stripe hosted checkout, supporting cards, Apple Pay, Google Pay, and Klarna. It provides tools to create payment links and query payment status.

Context Engine

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

Prefeitura PE Recife: CPOM

Enables querying official data from the Prefeitura de Recife (Recife City Hall) through a hosted, read-only MCP server. Supports MCP over HTTP with usage-based prepaid billing.

DevUtils MCP Server

Provides essential developer tools for workspace management, including advanced file searching, project structure analysis, and batch code editing. It enables users to efficiently navigate, analyze, and modify source code within their development environment.

spotify-mcp-server

Enables natural language control of Spotify, including search, playback, and device management, with robust error handling and automatic token refresh.

sui-mcp-server

Comprehensive MCP server for the Sui blockchain with 53 tools covering wallets, DeFi (Cetus, DeepBook), SuiNS, staking, validators, Move introspection, and full RPC. Enables natural language interaction with the entire Sui ecosystem.

pipedrive-mcp

MCP server for Pipedrive CRM providing 88 tools for full CRUD on deals, persons, organizations, activities, and more, with custom field resolution and safety guards.

whatsapp-mcp-server

A least-privilege, send-only MCP server for WhatsApp, backed by Meta's official WhatsApp Cloud API. It exposes tools to send text messages and pre-approved templates.

slack-mcp

MCP server that exposes Slack Web API operations as tools, enabling users to list conversations, read history, post messages, and call any Slack Web API method.

Rod MCP Server

Browser automation for AI agents via the Model Context Protocol, enabling web navigation, form filling, screenshots, and more using Chromium.

Mail Client MCP

作为 MCP 服务器的电子邮件客户端。特性：多重配置，不仅仅是 Gmail。 **Explanation of Choices:** * **电子邮件客户端 (diànzǐ yóujiàn kèhùduān):** This is the standard and most accurate translation of "Email Client." * **作为 (zuòwéi):** This translates to "as" in the sense of "functioning as" or "serving as." * **MCP 服务器 (MCP fúwùqì):** This directly translates "MCP Server." It's assumed the reader understands what an MCP server is in this context. * **特性 (tèxìng):** This is the standard translation of "Feature" in a technical context. * **多重配置 (duōchóng pèizhì):** This translates to "multiple configuration." It implies the ability to set up and manage several different configurations. * **不仅仅是 (bùjǐnjǐn shì):** This translates to "more than just" or "not only." * **Gmail:** This is a proper noun and remains the same in Chinese. Therefore, the translation accurately conveys the meaning of the original English phrase.

oh-my-mcp

A powerful MCP server with 116 practical tools across 9 categories including compression, web, file system, data processing, text, system, utilities, subagent AI, and browser automation.

Google-Calendar Universal MCP

Enables interaction with Google Calendar through a unified MCP API for managing events, calendars, and related tools.