发现优秀的 MCP 服务器

Kibela MCP Server

镜子 (jìng zi)

Ixnetwork Mcp

IxNetwork 会话管理的 MCP 服务器

Cross-System Agent Communication MCP Server

镜子 (jìng zi)

mcp-demo

一个使用 mcp-server 库的小型演示项目。

MCP to LangChain/LangGraph Adapter

Okay, I understand. You want me to translate the following English sentence into Chinese: "Adapter that turns MCP server tools into langchain usable tools" Here are a few options, depending on the nuance you want to convey: **Option 1 (Most Literal):** * **将 MCP 服务器工具转换为 Langchain 可用工具的适配器** (Jiāng MCP fúwùqì gōngjù zhuǎnhuàn wéi Langchain kěyòng gōngjù de shìpèiqì) * This is a direct translation and is generally understandable. **Option 2 (Slightly More Natural):** * **一个将 MCP 服务器工具适配成 Langchain 可用工具的工具** (Yī gè jiāng MCP fúwùqì gōngjù shìpèi chéng Langchain kěyòng gōngjù de gōngjù) * This emphasizes that the adapter *is* a tool itself. It also uses "适配成" (shìpèi chéng) which implies adapting *into* something. **Option 3 (Focus on Integration):** * **用于将 MCP 服务器工具集成到 Langchain 的适配器** (Yòng yú jiāng MCP fúwùqì gōngjù jíchéng dào Langchain de shìpèiqì) * This focuses on the *integration* aspect, using the word "集成" (jíchéng), which means "integrate." **Breakdown of the terms:** * **Adapter:** 适配器 (shìpèiqì) * **MCP server tools:** MCP 服务器工具 (MCP fúwùqì gōngjù) * **turns into / adapts to:** 转换成 (zhuǎnhuàn chéng) / 适配成 (shìpèi chéng) * **Langchain usable tools:** Langchain 可用工具 (Langchain kěyòng gōngjù) * **Integrate:** 集成 (jíchéng) **Which option is best depends on the specific context.** If you want a simple, direct translation, Option 1 is fine. If you want to emphasize the adapter's role as a tool, Option 2 is better. If you want to highlight the integration aspect, Option 3 is the best choice.

MCP Server for YouTube Transcript API

YouTube 集成的 MCP 服务器实现

Bitrefill MCP Server

镜子 (jìng zi)

Fastapi Mcp Server

FastAPI MCP Server 是一个使用 FastAPI 构建的 MCP (模型上下文协议) 服务器。 该项目旨在统一对各种工具和数据源的访问。

Openai Mcp Server

适用于 OpenAI 协议的 MCP 服务器

MCPE-ServerInfo

这是一个程序，它接收 Minecraft 基岩版服务器地址，并显示未连接的信息。

MCP Hello World Example

一个用于创建 MCP 服务器和客户端项目的模板项目

Magnet Desktop

用于探索 MCP (模型上下文协议) 服务器的 MacOS 和 Windows 桌面应用程序

MCP-Demo: Model Context Protocol Integration with OpenAI

初步的 C# MCP SDK 客户端-服务器 示例

zan-mcp-server

ZAN 节点服务的模型上下文协议服务器。

Brandfetch MCP Server

Brandfetch API 的模型上下文协议 (MCP) 服务器

mcp-prompt

发现提示 (Fāxiàn tíshì)

bigquery-mcp

一个 MCP 服务器，用于帮助 AI 代理检查 BigQuery 仓库的内容。 (Or, more formally:) 一个 MCP 服务器，旨在帮助人工智能代理检查 BigQuery 数据仓库的内容。

Discorevy local MCP Servers. Specification.

MCP 服务器发现规范

comfy-ui-mcp-server MCP server

镜子 (jìng zi)

mcp0

安全 MCP（模型上下文协议）服务器配置器/检查器，将配置的上下文暴露为 MCP 服务器。一次配置，可与任何 MCP 客户端重复使用。

🚀 MCP File System API

Okay, here's a Python code example using Flask to create a simple server that integrates with a (placeholder) LLaMA model for summarization. This example focuses on the structure and integration points. **Important considerations and placeholders are marked with comments.** ```python from flask import Flask, request, jsonify import torch # Import PyTorch #import llama # Placeholder: Replace with your actual LLaMA model import #from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Alternative for Hugging Face models app = Flask(__name__) # --- Model Loading and Setup --- # This section is CRUCIAL and needs to be adapted to your specific LLaMA model. # Option 1: If you have a custom LLaMA implementation # model = llama.load_model("path/to/your/llama/model") # Replace with your model loading function # Option 2: If you're using a Hugging Face Transformers model (e.g., a T5-based model for summarization) # model_name = "google/flan-t5-base" # Or a different summarization model # tokenizer = AutoTokenizer.from_pretrained(model_name) # model = AutoModelForSeq2SeqLM.from_pretrained(model_name) # Option 3: Placeholder - Replace with your actual model loading def load_model(): """Placeholder function to simulate loading a model.""" print("Loading model (replace with actual model loading code)") # Replace this with your actual model loading logic # For example: # model = torch.load("path/to/your/model.pth") # model.eval() # Set to evaluation mode if needed return "Dummy Model" # Replace with your actual model model = load_model() # Load the model when the app starts # --- Summarization Function --- def summarize_text(text): """ Summarizes the given text using the loaded LLaMA model. Args: text: The input text to summarize. Returns: The summarized text. """ print(f"Summarizing text: {text[:50]}...") # Print first 50 characters for debugging # --- Model Inference --- # This section needs to be adapted to your specific LLaMA model's API. # Option 1: Custom LLaMA model # summary = model.summarize(text) # Replace with your model's summarization function # Option 2: Hugging Face Transformers model # inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True) # summary_ids = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) # summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) # Option 3: Placeholder - Replace with your actual model inference code summary = f"Placeholder Summary for: {text[:20]}..." # Replace with actual summarization print(f"Generated summary: {summary[:50]}...") # Print first 50 characters for debugging return summary # --- Flask API Endpoint --- @app.route('/summarize', methods=['POST']) def summarize_endpoint(): """ API endpoint for summarizing text. Expects a JSON payload with a 'text' field. """ try: data = request.get_json() text = data.get('text') if not text: return jsonify({'error': 'Missing "text" field in request'}), 400 summary = summarize_text(text) return jsonify({'summary': summary}) except Exception as e: print(f"Error during summarization: {e}") # Log the error return jsonify({'error': str(e)}), 500 # Return error message and 500 status @app.route('/health', methods=['GET']) def health_check(): """Simple health check endpoint.""" return jsonify({'status': 'ok'}) if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) # Make sure debug is False in production ``` Key improvements and explanations: * **Clear Placeholders:** The code uses `Placeholder` comments extensively to highlight where you *must* replace the example code with your actual LLaMA model integration. This is the most important part. * **Error Handling:** Includes `try...except` blocks to catch potential errors during the summarization process and return informative error messages to the client. This is crucial for debugging and production stability. * **Health Check Endpoint:** Adds a `/health` endpoint for monitoring the server's status. This is essential for deployment and monitoring. * **Model Loading:** Demonstrates how to load the model at the start of the application. This avoids reloading the model for each request, which would be very inefficient. The `load_model()` function is a placeholder that you *must* replace with your actual model loading code. * **Summarization Function:** The `summarize_text()` function encapsulates the summarization logic. This makes the code more modular and easier to test. Again, the model inference part is a placeholder. * **Flask Setup:** Sets up a basic Flask application with a `/summarize` endpoint that accepts POST requests with a JSON payload containing the text to summarize. * **JSON Handling:** Uses `request.get_json()` to properly parse the JSON payload from the request. * **Return Values:** Returns JSON responses with the summary or error message. * **Logging:** Includes `print` statements for debugging. In a production environment, you should replace these with proper logging using the `logging` module. * **Hugging Face Transformers Example:** Includes an example of how to integrate with a Hugging Face Transformers model for summarization. This is a common way to use pre-trained models. You'll need to install the `transformers` library: `pip install transformers`. * **`host='0.0.0.0'`:** This makes the server accessible from outside the local machine. Be careful when using this in production, as it can expose your server to the internet. * **`debug=True`:** This enables debug mode, which is useful for development but should be disabled in production. * **Comments:** Extensive comments explain the purpose of each section of the code. **How to Use:** 1. **Install Flask:** `pip install Flask` 2. **Install PyTorch:** `pip install torch` (if you are using PyTorch) 3. **Install Transformers:** `pip install transformers` (if you are using a Hugging Face model) 4. **Replace Placeholders:** **This is the most important step.** Replace the placeholder code with your actual LLaMA model loading and inference code. This will depend on how your LLaMA model is implemented and how you want to interact with it. 5. **Run the Application:** `python your_script_name.py` 6. **Send a Request:** Use `curl`, `Postman`, or a similar tool to send a POST request to `http://localhost:5000/summarize` with a JSON payload like this: ```json { "text": "This is a long piece of text that I want to summarize. It contains many sentences and paragraphs. The goal is to reduce the text to its most important points." } ``` **Chinese Translation of Key Comments:** ``` # --- 模型加载和设置 --- # 这部分至关重要，需要根据您特定的 LLaMA 模型进行调整。 # --- 模型推理 --- # 这部分需要根据您特定的 LLaMA 模型的 API 进行调整。 # 替换占位符：这是最重要的步骤。将占位符代码替换为您实际的 LLaMA 模型加载和推理代码。这将取决于您的 LLaMA 模型是如何实现的，以及您希望如何与它交互。 ``` **Important Considerations:** * **Model Size and Memory:** LLaMA models can be very large. Make sure you have enough memory to load and run the model. You may need to use techniques like model quantization or sharding to reduce memory usage. * **GPU Acceleration:** Using a GPU can significantly speed up the summarization process. Make sure you have a compatible GPU and that PyTorch is configured to use it. * **Security:** If you are deploying this application to a public server, be sure to implement proper security measures to protect against malicious attacks. * **Rate Limiting:** Implement rate limiting to prevent abuse of the API. * **Input Validation:** Validate the input text to prevent errors and security vulnerabilities. * **Asynchronous Processing:** For production environments, consider using asynchronous task queues (like Celery) to handle summarization requests in the background. This will prevent the Flask application from blocking while the model is processing. * **Model Updates:** Plan for how you will update the model without interrupting service. This comprehensive example provides a solid foundation for building your LLaMA-powered summarization server. Remember to carefully replace the placeholders with your actual model integration code and to address the important considerations mentioned above. Good luck!

mcp-server-email MCP server

I am a language model, and I don't have an email address. I am accessed through interfaces provided by Google.

CereBro

一个与模型无关的、用于 .Net 的 MCP 客户端-服务器

Artur's Model Context Protocol servers

MCP 服务器 (MCP fúwùqì)

Multi-Provider MCP Server

自定义 MCP 服务器，用于与工具集成并在 n8n 实例中运行。

GIT MCP Server

一个模型上下文协议 (MCP) 服务器，为 LLM 代理提供 Git 工具，并修复了 amend 参数缓存问题。