Discover Awesome MCP Servers

DkwtMCP

Local MCP server that connects Claude Desktop with Garmin and Apple Health data to read training and recovery, estimate heart rate and pace zones, analyze performance, and create structured workouts.

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.

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.

Aws Sample Gen Ai Mcp Server

Okay, here's a sample code snippet demonstrating how to use Gen-AI (specifically, Bedrock) with an MCP (Message Communication Protocol) server in Python. This example assumes you have: 1. **AWS Credentials Configured:** Your AWS credentials (access key, secret key, region) are properly configured, either through environment variables, an IAM role, or the AWS CLI. 2. **Bedrock Access:** You have access to the Bedrock service and the specific model you want to use (e.g., Anthropic Claude, AI21 Labs Jurassic-2). 3. **MCP Server Running:** You have an MCP server running and listening for connections. The example uses a simple TCP socket for the MCP communication. You'll need to adapt the MCP server part to your specific MCP implementation. 4. **Libraries Installed:** You have the necessary libraries installed: `boto3`, `json`. ```python import boto3 import json import socket # Configuration BEDROCK_REGION = "us-east-1" # Replace with your Bedrock region MODEL_ID = "anthropic.claude-v2" # Replace with your desired Bedrock model ID MCP_SERVER_HOST = "localhost" # Replace with your MCP server host MCP_SERVER_PORT = 12345 # Replace with your MCP server port # Initialize Bedrock client bedrock = boto3.client(service_name="bedrock-runtime", region_name=BEDROCK_REGION) def generate_text_with_bedrock(prompt): """ Generates text using the Bedrock service. Args: prompt (str): The prompt to send to the model. Returns: str: The generated text, or None if there was an error. """ try: # Construct the request body based on the model if "anthropic" in MODEL_ID: body = json.dumps({ "prompt": f"\n\nHuman: {prompt}\n\nAssistant:", "max_tokens_to_sample": 200, # Adjust as needed "temperature": 0.5, # Adjust as needed "top_p": 0.9, # Adjust as needed }) content_type = "application/json" accept = "application/json" elif "ai21" in MODEL_ID: body = json.dumps({ "prompt": prompt, "maxTokens": 200, "temperature": 0.7, "topP": 1, "stopSequences": [] }) content_type = "application/json" accept = "application/json" else: print(f"Unsupported model ID: {MODEL_ID}") return None response = bedrock.invoke_model( modelId=MODEL_ID, contentType=content_type, accept=accept, body=body ) response_body = json.loads(response["body"].read().decode("utf-8")) # Extract the generated text based on the model if "anthropic" in MODEL_ID: generated_text = response_body["completion"] elif "ai21" in MODEL_ID: generated_text = response_body["completions"][0]["data"]["text"] else: return None return generated_text.strip() except Exception as e: print(f"Error generating text: {e}") return None def handle_mcp_request(client_socket): """ Handles a request received from the MCP server. Args: client_socket (socket): The socket connected to the client. """ try: # Receive data from the MCP server request_data = client_socket.recv(1024).decode("utf-8") # Adjust buffer size as needed if not request_data: print("No data received from MCP server.") return print(f"Received from MCP server: {request_data}") # Assuming the MCP request is a JSON string containing a "prompt" field try: request_json = json.loads(request_data) prompt = request_json.get("prompt") if not prompt: response_message = json.dumps({"error": "Missing 'prompt' field in request."}) client_socket.sendall(response_message.encode("utf-8")) return except json.JSONDecodeError: response_message = json.dumps({"error": "Invalid JSON format in request."}) client_socket.sendall(response_message.encode("utf-8")) return # Generate text using Bedrock generated_text = generate_text_with_bedrock(prompt) if generated_text: # Construct the response message response_message = json.dumps({"response": generated_text}) else: response_message = json.dumps({"error": "Failed to generate text."}) # Send the response back to the MCP server client_socket.sendall(response_message.encode("utf-8")) print(f"Sent to MCP server: {response_message}") except Exception as e: print(f"Error handling MCP request: {e}") error_message = json.dumps({"error": str(e)}) client_socket.sendall(error_message.encode("utf-8")) finally: client_socket.close() def main(): """ Main function to listen for connections from the MCP server. """ server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.bind((MCP_SERVER_HOST, MCP_SERVER_PORT)) server_socket.listen(1) # Listen for only one connection at a time (for simplicity) print(f"Listening for MCP server connections on {MCP_SERVER_HOST}:{MCP_SERVER_PORT}") while True: try: client_socket, address = server_socket.accept() print(f"Accepted connection from {address}") handle_mcp_request(client_socket) except KeyboardInterrupt: print("Shutting down server.") break except Exception as e: print(f"Error in main loop: {e}") server_socket.close() if __name__ == "__main__": main() ``` **Explanation:** 1. **Imports:** Imports necessary libraries: `boto3` for Bedrock, `json` for handling JSON data, and `socket` for MCP communication. 2. **Configuration:** Sets configuration variables for the Bedrock region, model ID, MCP server host, and port. **Important:** Replace these with your actual values. 3. **`generate_text_with_bedrock(prompt)`:** * Takes a `prompt` as input. * Constructs the request body for the Bedrock `invoke_model` API call. **Crucially, the format of the request body depends on the specific Bedrock model you're using.** The example shows how to format the request for Anthropic Claude and AI21 Labs Jurassic-2. Refer to the Bedrock documentation for the correct format for other models. * Calls `bedrock.invoke_model()` to send the request to Bedrock. * Parses the response from Bedrock and extracts the generated text. **Again, the format of the response depends on the model.** * Handles potential errors and returns the generated text or `None` if there was an error. 4. **`handle_mcp_request(client_socket)`:** * Receives data from the MCP server using `client_socket.recv()`. * Assumes the data is a JSON string containing a `prompt` field. Parses the JSON and extracts the prompt. Handles potential JSON decoding errors. * Calls `generate_text_with_bedrock()` to generate text using Bedrock. * Constructs a JSON response message containing either the generated text or an error message. * Sends the response back to the MCP server using `client_socket.sendall()`. * Closes the client socket. 5. **`main()`:** * Creates a TCP socket and binds it to the specified host and port. * Listens for incoming connections from the MCP server. * When a connection is accepted, calls `handle_mcp_request()` to handle the request. * Handles `KeyboardInterrupt` to allow the server to be shut down gracefully. **How to Use:** 1. **Install Libraries:** ```bash pip install boto3 ``` 2. **Configure AWS Credentials:** Make sure your AWS credentials are set up correctly. 3. **Start the MCP Server:** Start your MCP server and make sure it's listening on the specified host and port. The MCP server needs to be able to send JSON requests to this Python script. 4. **Run the Python Script:** Run the Python script. It will listen for connections from the MCP server. 5. **Send Requests from the MCP Server:** Send JSON requests to the Python script from your MCP server. The requests should have the following format: ```json { "prompt": "Write a short story about a cat who goes on an adventure." } ``` **Important Considerations:** * **Error Handling:** The code includes basic error handling, but you should add more robust error handling for production use. Consider logging errors to a file or using a more sophisticated error reporting system. * **Security:** If your MCP server is exposed to the internet, you need to implement proper security measures to protect it from unauthorized access. * **Scalability:** This example is a simple single-threaded server. For production use, you'll likely need to use a multi-threaded or asynchronous server to handle multiple requests concurrently. Consider using a framework like `asyncio` or `threading` for this. * **MCP Protocol:** This example assumes a very simple MCP protocol where the request is a JSON string. You'll need to adapt the code to your specific MCP protocol. * **Bedrock Model Parameters:** Experiment with the parameters in the `generate_text_with_bedrock` function (e.g., `max_tokens_to_sample`, `temperature`, `top_p`) to get the desired results from the Bedrock model. Refer to the Bedrock documentation for details on these parameters. * **Model-Specific Code:** The code includes model-specific logic for Anthropic Claude and AI21 Labs Jurassic-2. You'll need to add similar logic for other Bedrock models you want to use. **Always consult the Bedrock documentation for the specific model you're using.** * **Rate Limiting:** Be aware of the rate limits for the Bedrock service. You may need to implement rate limiting in your code to avoid exceeding the limits. * **Cost:** Using Bedrock incurs costs. Be sure to monitor your AWS usage and costs. This example provides a starting point for integrating Gen-AI (Bedrock) with an MCP server. You'll need to adapt it to your specific requirements and environment.

Openfort MCP Server

Enables AI assistants to interact with Openfort's wallet infrastructure, allowing them to create projects, manage configurations, generate wallets and users, and query documentation through 42 integrated tools.

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.

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.

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.

spotify-mcp-server

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

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.

automatised-pipeline

A Rust MCP server that indexes codebases into a property graph and provides tools for code intelligence, such as searching, context, impact analysis, and change detection.

viraill-mcp

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

gmail-fast-mcp

A Gmail MCP server providing 19 tools for email operations, label and filter management, and attachment handling via the Gmail API.

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.

Ametller Origen

Enables shopping Ametller Origen online groceries through Claude, allowing catalog browsing, cart management, and order history access, with payment handled directly on the retailer's site.

Amazon Marketplace MCP Server by CData

This read-only MCP Server allows you to connect to Amazon Marketplace data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

MCP ArangoDB

A Model Context Protocol server for ArangoDB, providing full database access through MCP's standardized tool interface.

mcp-server-vector-search

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

AI Carousel Generator

Enables AI clients to generate premium 4K carousel slides from a single instruction, combining expert copywriting, AI image generation, and premium typography compositing.

chrome-orchestrator

Enables AI agents to dynamically provision headless browser environments and connect to them through a unified HTTP/SSE MCP endpoint with persistent storage and automatic health monitoring.

sliverc2-mcp

sliverc2-mcp

discord-mcp

MCP server over the real Discord REST API: 5 read-only tools plus 7 write tools gated off by default behind DISCORD_MCP_ENABLE_WRITE.

Rod MCP Server

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

Google Analytics MCP for Coolify

Provides a secure FastMCP gateway to Google Analytics Admin and Data APIs, deployable on Coolify with OAuth protection and encrypted storage.

agentmako

Local-first codebase intelligence engine providing AI coding agents with a typed MCP toolset for understanding and navigating code repositories.

BlackDome MCP Server

Give your AI agents direct access to live honeypot threat intelligence. Look up attacker IPs, browse indicators of compromise (IOCs), inspect captured credentials and malware payloads, profile threat actors, and render a real-time global attack map — all from Claude, Cursor, or any MCP-compatible client.

LiblibAI Picture Generator

Enables AI image generation through LiblibAI API with natural language prompts. Supports various art styles, real-time progress tracking, and account credit management.

Gemini Code Assist MCP

An MCP server that integrates Google Gemini CLI with Claude Code for AI-powered development assistance, enabling code review, bug analysis, feature planning, and code explanation without requiring an API key.

kcc-epub-converter

Converts large comic archives (CBZ/CBR) into Kindle-optimized EPUB volumes with GPU acceleration, smart chunking under Amazon's 200MB limit, and device-specific profiles for manga/comics.

Gentou

MCP server for generating images using Fal AI's nano-banana-pro model. Enables fast image generation from natural language prompts with customizable aspect ratio, count, and format.