Discover Awesome MCP Servers

APEX MCP Server

Enables AI assistants to interact with the APEX AI operating system to manage waitlist registrations, request demos, and retrieve detailed documentation. It provides tools for founders to explore the autonomous digital twin platform and its integrations with email, Slack, and CRMs.

eu-regulations

Query 37 EU regulations — from GDPR and AI Act to DORA, MiFID II, eIDAS, Medical Device Regulation, and more — directly from Claude, Cursor, or any MCP-compatible client.

Greenplum MCP Server by CData

Greenplum MCP Server by CData

base-gasless-deploy-mcp

Gasless ERC-20 token deployment on Base using CDP Paymaster. Deploy tokens, verify contracts, check status.

hyperspell-mcp

Hyperspell 让你将 AI 驱动的应用程序连接到任何数据——它是非结构化和半结构化数据的 Plaid。

EnrichB2B MCP Server

一个实现了模型上下文协议的服务器，使用户能够通过 EnrichB2B API 检索 LinkedIn 个人资料信息和活动数据，并使用 OpenAI GPT-4 或 Anthropic Claude 模型生成文本。

product-hunt-mcp

product-hunt-mcp

opendart-fss-mcp

An MCP server that provides access to Korea's DART corporate disclosure system, offering 84 tools for retrieving financial statements, periodic reports, and shareholding information. It enables users to programmatically query and analyze official Korean corporate data via the OpenDART API.

Health MCP Server

Aggregates and analyzes fitness data from multiple sources like Whoop and Strava through a modular adapter architecture. It enables users to monitor health metrics, track activities, and gain insights into sleep, recovery, and training performance.

Hava Durumu MCP Server

Provides weather data using Open-Meteo API with support for current weather, 24-hour forecasts, and 7-day forecasts for Turkish cities and coordinate-based queries.

medRxiv MCP Server

镜子 (jìng zi)

Spotify MCP Server

MCP 服务器，用于与 Splunk 交互

hive

Context infrastructure for AI-assisted development — on-demand Obsidian vault access via MCP

Weather MCP Server

Provides real-time weather information and forecasts, connecting AI assistants with live weather data for current conditions and multi-day forecasts for any location worldwide.

Cursor10x MCP

The Cursor10x Memory System creates a persistent memory layer for AI assistants (specifically Claude), enabling them to retain and recall short-term, long-term and episodic memory on autonomously.

Wealthfolio MCP Server

Enables AI-powered portfolio analysis for Wealthfolio, allowing Claude to query and analyze investment holdings, asset allocation, real estate properties, and execute transactions through natural language.

Customer Registration MCP Server

Enables creating and managing customer records via an external API with Bearer token authentication, supporting required fields (name, email, phone) and extensive optional data including addresses, UTM parameters, and tags.

TestRail MCP Server

Enables comprehensive TestRail test management integration with support for projects, test cases, runs, results, advanced reporting, analytics, and AutoSpectra test automation framework synchronization.

aiohttp-mcp

构建在 aiohttp 之上的模型上下文协议 (MCP) 服务器的工具： Here are some tools and libraries that can help you build Model Context Protocol (MCP) servers on top of aiohttp: * **aiohttp:** This is the fundamental asynchronous HTTP server and client library for Python. You'll use it to handle incoming MCP requests and send responses. You'll need to understand how to define routes, handle requests, and serialize/deserialize data. * **asyncio:** Since aiohttp is built on asyncio, you'll need a good understanding of asynchronous programming concepts like event loops, coroutines, and tasks. This is crucial for handling concurrent requests efficiently. * **Marshmallow (or similar serialization library):** MCP often involves structured data. Marshmallow is a popular library for serializing and deserializing Python objects to and from formats like JSON. This helps you validate incoming requests and format outgoing responses according to the MCP specification. Alternatives include `attrs` with `cattrs`, or `pydantic`. * **JSON Schema (and a validator):** MCP implementations often use JSON Schema to define the structure and validation rules for the request and response payloads. Libraries like `jsonschema` can be used to validate incoming requests against a schema, ensuring that they conform to the MCP specification. * **gRPC (optional, but relevant for comparison):** While you're building on aiohttp, it's worth understanding gRPC. gRPC is a high-performance RPC framework that's often used for similar purposes as MCP. Understanding gRPC can help you make informed design decisions about your MCP implementation. If performance is critical, consider whether gRPC might be a better fit than a custom aiohttp-based solution. * **Logging:** Use Python's built-in `logging` module to log requests, errors, and other relevant information. This is essential for debugging and monitoring your MCP server. * **Testing Framework (pytest, unittest):** Write unit tests and integration tests to ensure that your MCP server is working correctly. `pytest` is a popular and flexible testing framework. * **OpenAPI/Swagger (optional):** If you want to document your MCP API, you can use OpenAPI (formerly Swagger). Tools like `aiohttp-apispec` can help you generate OpenAPI specifications from your aiohttp routes. This makes it easier for clients to understand and use your MCP server. **Example (Conceptual):** ```python import asyncio import json from aiohttp import web import marshmallow import jsonschema # Define your data models using Marshmallow class MyRequestSchema(marshmallow.Schema): input_data = marshmallow.fields.String(required=True) class MyResponseSchema(marshmallow.Schema): output_data = marshmallow.fields.String(required=True) # Define your JSON Schema (alternative to Marshmallow for validation) request_schema = { "type": "object", "properties": { "input_data": {"type": "string"} }, "required": ["input_data"] } async def handle_mcp_request(request): try: data = await request.json() # Option 1: Validate with JSON Schema try: jsonschema.validate(instance=data, schema=request_schema) except jsonschema.exceptions.ValidationError as e: return web.json_response({"error": str(e)}, status=400) # Option 2: Validate and deserialize with Marshmallow # try: # validated_data = MyRequestSchema().load(data) # except marshmallow.exceptions.ValidationError as err: # return web.json_response({"errors": err.messages}, status=400) # Process the request (replace with your actual logic) input_data = data['input_data'] # or validated_data['input_data'] output_data = f"Processed: {input_data}" # Serialize the response with Marshmallow response_data = MyResponseSchema().dump({"output_data": output_data}) return web.json_response(response_data) except Exception as e: print(f"Error: {e}") return web.json_response({"error": "Internal Server Error"}, status=500) async def main(): app = web.Application() app.add_routes([web.post('/mcp', handle_mcp_request)]) runner = web.AppRunner(app) await runner.setup() site = web.TCPSite(runner, 'localhost', 8080) await site.start() print("Server started on http://localhost:8080") await asyncio.Future() # Run forever if __name__ == '__main__': asyncio.run(main()) ``` **Key Considerations for MCP:** * **Specification Adherence:** Carefully review the MCP specification you're implementing. Pay close attention to the required data formats, error codes, and communication protocols. * **Error Handling:** Implement robust error handling to gracefully handle invalid requests, unexpected errors, and other issues. Return informative error messages to the client. * **Security:** Consider security implications, especially if your MCP server is exposed to the internet. Implement authentication, authorization, and input validation to protect against malicious attacks. * **Performance:** Optimize your code for performance, especially if you expect a high volume of requests. Use asynchronous programming effectively, and consider caching frequently accessed data. * **Scalability:** Design your MCP server to be scalable, so that it can handle increasing traffic. Consider using a load balancer and multiple instances of your server. * **Monitoring:** Implement monitoring to track the performance and health of your MCP server. Use metrics like request latency, error rates, and resource utilization to identify and resolve issues. This comprehensive list should give you a good starting point for building your MCP server on top of aiohttp. Remember to adapt the tools and techniques to the specific requirements of your MCP implementation.

Minted MCP Server

Enables interaction with Minted.com to retrieve address book contacts, order history, and delivery information for recent card orders.

Webots MCP Server

Enables real-time monitoring and control of any Webots robot simulation, providing access to robot state, sensors, camera feeds, and simulation controls (pause, resume, reset) through natural language.

MCP Workflow Tracker

Provides observability for multi-agent workflows by tracking hierarchical task structure, architectural decisions, reasoning, encountered problems, code modifications with Git diffs, and temporal metrics.

LibreOffice MCP Server

Enables AI assistants to create, read, convert, and manipulate LibreOffice documents programmatically, supporting 50+ file formats including Writer, Calc, Impress documents with real-time editing, batch operations, and document analysis capabilities.

2slides MCP Server

Enables users to generate presentation slides using 2slides.com's API through Claude Desktop. Supports searching for slide themes, generating slides from text input, and monitoring job status for slide creation.

armavita-quo-mcp

Local-first MCP server for Quo/OpenPhone automation across messaging, calls, contacts, users, phone numbers, and webhooks via a clean stdio tool surface.

Discord MCP Server

A secure server that enables interaction with Discord channels through JWT-authenticated API calls, allowing users to send messages, fetch channel data, search content, and perform moderation actions.

mcp-gitpro

A context-efficient GitHub MCP server designed for AI agents to manage repositories, issues, pull requests, and actions directly via cloud workflows. It focuses on a compact tool surface to minimize context waste while providing comprehensive read and write coverage without requiring a local Git CLI.

Obsidian MCP

Enables AI agents to manage Obsidian vaults through full CRUD operations, wikilink management, and section-level manipulation. It supports frontmatter editing, tag-based searching, and automated link updates to maintain vault integrity.

Z-Image Studio

A local text-to-image generation server that enables AI agents to generate images, manage models, and browse history using the Z-Image-Turbo model. It supports hardware acceleration across NVIDIA, Apple Silicon, and AMD GPUs via multiple MCP transport protocols.