Discover Awesome MCP Servers

Red MCP Server

Connects AI assistants to Big Red Cloud accounting data, allowing natural-language queries and controlled write operations with safety checks.

Geo MCP Worker

Free geospatial MCP server for AI agents, providing geocoding, reverse geocoding, POI search, and route planning using OpenStreetMap data via Nominatim, Overpass, and OSRM.

VirusTotal MCP Server

Enables security analysis of URLs, files, IPs, and domains using the VirusTotal API, with automatic fetching of relationship data to provide comprehensive reports.

Hashkey MCP Server

A Model Context Protocol server that provides onchain tools for AI applications to interact with the Hashkey Network, enabling cryptocurrency transfers, smart contract deployment, and blockchain interactions.

figma-mcp-server

Connects Figma to Claude via MCP for auditing design systems, checking naming conventions, and analyzing component consistency using the public Figma API.

Whissle MCP Server

A Python-based server that provides access to Whissle API endpoints for speech-to-text, diarization, translation, and text summarization.

mcp-sam-gov

The most comprehensive keyless federal-data MCP server. 36 tools for SAM.gov + USAspending + Federal Register + eCFR + Grants.gov. No API key, no registration, no signup. Works in Claude Desktop, Claude Code, Codex CLI, Cursor, Continue, Gemini CLI, and any MCP-aware host.

finanal-mcp

A minimal, token-efficient MCP server that combines stock prices and fundamentals with the macro and micro narrative around them — so an AI agent can reason about why a stock moved, what people are thinking about it, and what probable scenarios lie ahead.

Claude-NWS Protocol Bridge

Integrates the US National Weather Service API with Claude Desktop to provide real-time weather conditions, forecasts, and detailed weather metrics for any US location through natural language queries.

MCP DS Toolkit Server

A standalone MCP server that brings complete data science capabilities to AI assistants, enabling them to load data, train models, and track experiments through natural language.

Application Tracker MCP

A privacy-first MCP server for locally managing job, fellowship, and graduate-school applications. It offers tools for tracking application status, analyzing role fit, generating LaTeX CV/cover letters, interview prep, and discovering public jobs from ATS APIs.

md-gitlab-mcp

A fully-featured GitLab MCP server that provides 25 advanced capabilities for interacting with GitLab via REST API, including repositories, merge requests, issues, and global search.

TalkDB

Enables natural language querying of databases with multi-turn conversations, auto-generated charts, and proactive monitoring via scheduled queries and alerts.

Notion MCP Server

Enables AI agents to interact with Notion workspaces through the Notion API, allowing them to search, read, create, update pages and databases, and manage comments using natural language commands.

skim-mcp

Provides code compression and analysis tools for Claude Code, reducing token usage while preserving code structure.

Dev.to MCP Server

An MCP server implementation that allows AI assistants to access, search, and interact with Dev.to content, including fetching articles, retrieving user information, and publishing new content.

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.

openai-image-mcp

MCP server that wraps OpenAI's GPT Image API to generate context-appropriate images for PPT/Word documents via a single generate_image tool.

ui-diff-mcp

Enables comparison of mobile app screenshots against design mockups, with Calorix as the first integration target.

3DiVi Face SDK MCP Server

Local MCP server for on-premise face recognition, liveness detection, and identity verification, enabling AI agents to use 3DiVi Face SDK tools without cloud dependency or SDK integration code.

WaveGuard

An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.

CamelMailer MCP Server

Lets AI assistants send and inspect transactional email via the CamelMailer platform.

mcp-ssh

MCP server for SSH remote execution, file transfer, and file editing with automatic backup/trash and ~/.ssh/config integration.

barcelona-events

MCP server to access Barcelona events data via Pipeworx gateway.

Karya

An MCP server that gives AI agents hands to make phone calls, send messages, and handle CRM/task workflows. It runs with a simulated provider for zero-cost testing and can switch to real providers like Twilio and ElevenLabs.

AynOps

CyberSecurity MCP Server extends Claude with real-time cybersecurity reconnaissance capabilities that Claude doesn't have by default. Instead of manually running 5 different tools across different terminals, just tell Claude "analyze google.com" and get a complete security breakdown instantly. Tools included: * WHOIS Lookup — registrar, ownership, creation/expiry dates * DNS Enumeration — A,

mcp-document-intelligence

Enables AI assistants to analyze, rename, categorize, and organize local documents using OCR, metadata extraction, and batch processing, with preview mode and undo support.

app.wishpool/portugal-payments-mcp

Enables AI agents to accept payments in Portugal (Multibanco, cards, Apple Pay) via Stripe hosted checkout.

mcp-demo-server

Provides a weather lookup tool and greeting resource via MCP, enabling AI agents to fetch current conditions and personalized messages through natural language.

iaas-conversion-mcp

A Model Context Protocol (MCP) server that converts Confluence pages into standardized formats for the IaaS Storage team.