发现优秀的 MCP 服务器

jira-mcp

Enables headless Jira Cloud automation via MCP: create issues and subtasks, read and JQL-search issues, inspect agile boards and swimlanes, move issues through workflows, and add comments and attachments.

World Clock Pro MCP

A read-only MCP server that gives AI agents accurate current time, timezone conversions, DST transitions, and city lookups using live IANA timezone data, with cited sources.

repo-index-mcp

A local codebase retrieval tool that indexes a git repository into SQLite and provides query and retrieval capabilities over MCP stdio, enabling coding agents to search and retrieve code chunks from local repositories without external data transmission.

agentsvc-mcp

20 pay-per-call utility tools for AI agents via x402 USDC micropayments on Base. Screenshots, OCR, PDF, web scraping, weather, forex rates, crypto/stock prices, DNS, geocoding, translation, and more. $0.001–$0.008 per call. No API keys, no signup.

float-mcp

A community MCP server for float.com.

Runware MCP Server

Enables lightning-fast AI image and video generation, upscaling, background removal, captioning, and masking through the Runware API with automatic model selection and comprehensive validation.

PortOne MCP Server

面向开发者的 PortOne MCP 服务器

Prefeitura SP São Paulo: Emissão Parcela Simples Nacional

Enables users to consult official data on the issuance of Simples Nacional parcels from São Paulo city hall. Provides a read-only MCP server with a single tool for this query.

Semantic Mesh Memory (SEM) MCP Server

Provides a coherent memory layer for LLM agents that models beliefs as nodes in a constraint network to detect and surface semantic contradictions. It uses local embeddings and a hybrid geometric-logical energy model to identify conflicting information that requires review.

RetireGolden MCP

Headless MCP server for the RetireGolden retirement-planning calculator, providing typed tools to build/validate plans, run projections, Monte Carlo simulations, and optimization via stdio.

Tability MCP Server

Enables AI assistants to manage OKRs, track goals, and update progress on the Tability platform using natural language. It provides 27 tools for interacting with plans, objectives, outcomes, initiatives, and workspace memberships via the Tability API.

mcp-memory-gateway

An MCP server that provides memory management and gateway capabilities, enabling persistent storage and retrieval of contextual information across sessions.

Bonsai MCP

Connects MCP clients to a local Blender + Bonsai (BlenderBIM) session for scene inspection, IFC project querying, viewport screenshots, and executing Python code in Blender.

Multi-Model Advisor

决策模型委员会 / 决策模型理事会

frontend-mcp

Enables frontend navigation and browser automation through a deterministic MCP server, integrating Browser Use and optional code graph hints for coding agents.

Poke-MCP

一个模型上下文协议服务器，通过连接到 PokeAPI 提供宝可梦信息，使用户能够查询详细的宝可梦数据、发现随机宝可梦，以及按地区或类型查找宝可梦。

odoo-mcp-uvx

A read-only MCP server that enables AI assistants to query Odoo instances via XML-RPC, supporting search, read, count, and field inspection without requiring custom modules.

MCP Claude Code Conversation History

Enables Claude Code to search, browse, and read its own past conversation history across all projects using BM25 keyword search.

datasaudi-mcp

Ask about the Saudi economy in plain English or Arabic. No API key or database required.

dynu-mcp

Enables management of Dynu services (DNS, email, compute, domain, monitor, ping) via 117 MCP tools that wrap the full Dynu REST API v2, requiring an API key per request.

archgraph

Serves the architectural graph of a hexagonal-architecture codebase to coding agents via stdio, providing tools to analyze dependencies, check violations, and inspect structural changes.

MySQL-Performance-Tuner-Mcp

MySQL MCP Performance Tuning Server - AI-powered MySQL performance tuning capabilities

file-reader-mcp

Enables LLM agents to securely explore, read, search, and analyze text-based files within a sandboxed environment.

YouTube Transcript MCP Server

There are a few ways to approach building an MCP (Microservices Communication Protocol) server for fetching YouTube transcripts. Here's a breakdown of the concepts and potential implementations: **Understanding the Requirements** * **YouTube Data API:** You'll need to use the YouTube Data API to retrieve transcript information. This API requires authentication (API key or OAuth 2.0). * **Transcript Retrieval:** The API provides different ways to get transcripts: * **Automatic Transcripts (ASR):** Generated by YouTube's automatic speech recognition. These are often less accurate. * **Community Contributions:** Transcripts provided by the YouTube community. * **Official Transcripts:** Transcripts uploaded by the video creator. * **MCP (Microservices Communication Protocol):** This defines how your server will communicate with other microservices in your architecture. Common choices include: * **REST (HTTP):** Simple, widely understood. Good for basic operations. * **gRPC:** High-performance, uses Protocol Buffers for data serialization. Excellent for complex data structures and demanding performance. * **Message Queues (e.g., RabbitMQ, Kafka):** Asynchronous communication. Useful for decoupling services and handling large volumes of requests. * **Scalability and Reliability:** Consider how your server will handle a large number of requests and potential failures. * **Error Handling:** Implement robust error handling to gracefully deal with API errors, network issues, and invalid requests. **Implementation Options** Here are a few implementation options, focusing on different MCP approaches: **1. REST (HTTP) based MCP Server (Python with Flask/FastAPI)** * **Language:** Python (popular for API development) * **Framework:** Flask (simple) or FastAPI (modern, asynchronous) ```python # FastAPI example from fastapi import FastAPI, HTTPException from youtube_transcript_api import YouTubeTranscriptApi app = FastAPI() @app.get("/transcript/{video_id}") async def get_transcript(video_id: str, lang: str = 'en'): """ Fetches the transcript for a YouTube video. Args: video_id: The YouTube video ID. lang: The desired language of the transcript (default: 'en'). Returns: A list of transcript entries (text, start, duration). """ try: transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=[lang]) return transcript except Exception as e: raise HTTPException(status_code=500, detail=str(e)) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) ``` **Explanation:** * **`YouTubeTranscriptApi`:** This is a Python library that simplifies interacting with the YouTube transcript API. Install it with `pip install youtube-transcript-api`. * **`FastAPI`:** A modern, high-performance web framework for building APIs. * **`/transcript/{video_id}`:** An endpoint that accepts the YouTube video ID as a path parameter. * **`lang`:** An optional query parameter to specify the desired language. * **Error Handling:** The `try...except` block catches potential errors and returns an HTTP 500 error with a descriptive message. * **`uvicorn`:** An ASGI server to run the FastAPI application. **To use this:** 1. Install dependencies: `pip install fastapi uvicorn youtube-transcript-api` 2. Run the server: `python your_script_name.py` 3. Access the API: `http://localhost:8000/transcript/VIDEO_ID` (replace `VIDEO_ID` with the actual YouTube video ID). You can also specify the language: `http://localhost:8000/transcript/VIDEO_ID?lang=fr` **2. gRPC based MCP Server (Python with gRPC)** * **Language:** Python * **Framework:** gRPC **Steps:** 1. **Define the Protocol Buffer (.proto) file:** This defines the service and message structure. ```protobuf syntax = "proto3"; package youtube_transcript; service TranscriptService { rpc GetTranscript (TranscriptRequest) returns (TranscriptResponse) {} } message TranscriptRequest { string video_id = 1; string language = 2; } message TranscriptResponse { repeated TranscriptEntry entries = 1; } message TranscriptEntry { string text = 1; double start = 2; double duration = 3; } ``` 2. **Generate gRPC code:** Use the `grpc_tools.protoc` compiler to generate Python code from the `.proto` file. ```bash python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. youtube_transcript.proto ``` 3. **Implement the gRPC server:** ```python # youtube_transcript_server.py import grpc from concurrent import futures from youtube_transcript_api import YouTubeTranscriptApi import youtube_transcript_pb2 as youtube_transcript_pb2 import youtube_transcript_pb2_grpc as youtube_transcript_pb2_grpc class TranscriptServicer(youtube_transcript_pb2_grpc.TranscriptServiceServicer): def GetTranscript(self, request, context): try: transcript = YouTubeTranscriptApi.get_transcript(request.video_id, languages=[request.language]) entries = [] for entry in transcript: entries.append(youtube_transcript_pb2.TranscriptEntry( text=entry['text'], start=entry['start'], duration=entry['duration'] )) return youtube_transcript_pb2.TranscriptResponse(entries=entries) except Exception as e: context.abort(grpc.StatusCode.INTERNAL, str(e)) def serve(): server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) youtube_transcript_pb2_grpc.add_TranscriptServiceServicer_to_server(TranscriptServicer(), server) server.add_insecure_port('[::]:50051') server.start() server.wait_for_termination() if __name__ == '__main__': serve() ``` 4. **Implement the gRPC client (example):** ```python # youtube_transcript_client.py import grpc import youtube_transcript_pb2 as youtube_transcript_pb2 import youtube_transcript_pb2_grpc as youtube_transcript_pb2_grpc def get_transcript(video_id, language): with grpc.insecure_channel('localhost:50051') as channel: stub = youtube_transcript_pb2_grpc.TranscriptServiceStub(channel) request = youtube_transcript_pb2.TranscriptRequest(video_id=video_id, language=language) try: response = stub.GetTranscript(request) for entry in response.entries: print(f"[{entry.start:.2f} - {entry.start + entry.duration:.2f}] {entry.text}") except grpc.RpcError as e: print(f"Error: {e.details()}") if __name__ == '__main__': get_transcript("VIDEO_ID", "en") # Replace with a real video ID ``` **Explanation:** * **`.proto` file:** Defines the service (`TranscriptService`) and the messages (`TranscriptRequest`, `TranscriptResponse`, `TranscriptEntry`). * **`grpc_tools.protoc`:** Compiles the `.proto` file into Python code. * **`TranscriptServicer`:** Implements the `GetTranscript` method, which retrieves the transcript using `YouTubeTranscriptApi` and converts it into the gRPC response format. * **gRPC Client:** Connects to the server, sends a `TranscriptRequest`, and prints the received transcript entries. **To use this:** 1. Install dependencies: `pip install grpcio grpcio-tools protobuf youtube-transcript-api` 2. Compile the `.proto` file: `python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. youtube_transcript.proto` 3. Run the server: `python youtube_transcript_server.py` 4. Run the client: `python youtube_transcript_client.py` **3. Message Queue based MCP Server (Python with RabbitMQ/Kafka)** * **Language:** Python * **Message Queue:** RabbitMQ or Kafka **Conceptual Outline (RabbitMQ Example):** 1. **Producer (Client):** Sends a message to the queue with the video ID and language. 2. **Consumer (Server):** Listens to the queue, receives the message, fetches the transcript, and potentially publishes the transcript to another queue or stores it in a database. **RabbitMQ Example (Simplified):** * **Producer (Client):** ```python # producer.py import pika import json connection = pika.BlockingConnection(pika.ConnectionParameters('localhost')) channel = connection.channel() channel.queue_declare(queue='transcript_requests') message = {'video_id': 'VIDEO_ID', 'language': 'en'} # Replace with a real video ID channel.basic_publish(exchange='', routing_key='transcript_requests', body=json.dumps(message)) print(" [x] Sent %r" % message) connection.close() ``` * **Consumer (Server):** ```python # consumer.py import pika import json from youtube_transcript_api import YouTubeTranscriptApi connection = pika.BlockingConnection(pika.ConnectionParameters('localhost')) channel = connection.channel() channel.queue_declare(queue='transcript_requests') def callback(ch, method, properties, body): message = json.loads(body.decode('utf-8')) video_id = message['video_id'] language = message['language'] print(f" [x] Received request for video ID: {video_id}, language: {language}") try: transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=[language]) # Process the transcript (e.g., store in a database, publish to another queue) print(f" [x] Transcript fetched successfully for {video_id}") # Example: Print the first few lines for i in range(min(5, len(transcript))): print(transcript[i]) except Exception as e: print(f" [x] Error fetching transcript: {e}") channel.basic_consume(queue='transcript_requests', on_message_callback=callback, auto_ack=True) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming() ``` **Explanation:** * **RabbitMQ:** A message broker that allows asynchronous communication between services. * **`transcript_requests` queue:** The queue where the client sends requests for transcripts. * **Producer:** Sends a JSON message containing the video ID and language to the queue. * **Consumer:** Listens to the queue, retrieves the message, fetches the transcript using `YouTubeTranscriptApi`, and processes the transcript. * **`auto_ack=True`:** Automatically acknowledges the message after it's processed. Consider using manual acknowledgements for more robust error handling. **To use this:** 1. Install RabbitMQ: Follow the instructions on the RabbitMQ website. 2. Install dependencies: `pip install pika youtube-transcript-api` 3. Run the consumer: `python consumer.py` 4. Run the producer: `python producer.py` **Key Considerations and Best Practices** * **API Key Management:** Store your YouTube Data API key securely (e.g., environment variables, secrets management). Never hardcode it in your code. * **Rate Limiting:** The YouTube Data API has rate limits. Implement retry logic with exponential backoff to handle rate limit errors gracefully. Consider caching transcripts to reduce API calls. * **Error Handling:** Implement comprehensive error handling to catch API errors, network issues, and invalid requests. Log errors for debugging. * **Asynchronous Operations:** For gRPC and message queue implementations, use asynchronous operations (e.g., `asyncio` in Python) to improve performance and scalability. * **Data Validation:** Validate the input (video ID, language) to prevent errors and security vulnerabilities. * **Logging:** Use a logging library (e.g., `logging` in Python) to log important events and errors. * **Monitoring:** Monitor the performance of your server (e.g., request latency, error rates) to identify and address issues. * **Security:** If your server handles sensitive data, implement appropriate security measures (e.g., authentication, authorization, encryption). * **Scalability:** Design your server to be scalable to handle a large number of requests. Consider using a load balancer and multiple instances of your server. * **Deployment:** Choose a suitable deployment environment (e.g., cloud platform, containerization with Docker). * **Caching:** Implement caching mechanisms (e.g., Redis, Memcached) to store frequently accessed transcripts and reduce the load on the YouTube Data API. Consider using a cache invalidation strategy. * **Transcript Availability:** Not all YouTube videos have transcripts available. Handle cases where a transcript is not found. * **Language Support:** The `YouTubeTranscriptApi` library supports multiple languages. Allow users to specify the desired language. * **Transcript Types:** Consider supporting different types of transcripts (automatic, community, official). The `YouTubeTranscriptApi` library provides methods to access different transcript types. **Choosing the Right Approach** * **REST (HTTP):** Good for simple use cases and when you need a widely accessible API. Easy to implement and debug. * **gRPC:** Best for high-performance communication between microservices. Requires more setup but offers significant performance benefits. * **Message Queue:** Ideal for asynchronous processing and decoupling services. Useful for handling large volumes of requests and ensuring that requests are processed even if one service is temporarily unavailable. The best approach depends on your specific requirements and the overall architecture of your microservices. Start with REST if you're unsure, and then consider gRPC or message queues if you need better performance or scalability. Remember to prioritize security, error handling, and rate limiting in all implementations.

OmniDocs MCP

An intelligent MCP server that enables AI agents to crawl, index, and semantically search official framework documentation using local RAG. It prevents hallucinations by providing precise, up-to-date documentation excerpts directly into the AI's context window.

Crypto Price & Market Analysis MCP Server

Provides comprehensive cryptocurrency analysis using the CoinCap API, offering real-time price data, market analysis across exchanges, and historical price trends for any cryptocurrency.

trendforge-mcp

Enables querying YouTube gaming opportunity rankings to answer 'what should I record?' by providing tools for top games, game breakdowns with AI video ideas, and live topic checks.

alert-my-human

A lightweight MCP server that lets agents send email alerts for notifying humans when scheduled jobs fail, data is missing, or attention is needed.

postman-mcp

A Model Context Protocol (MCP) server that provides seamless integration with the Postman API, enabling AI assistants and applications to interact with Postman workspaces, collections, requests, environments, and folders programmatically.