Discover Awesome MCP Servers

Buildkite MCP Server

Buildkite連携のためのモデルコンテキストプロトコル (MCP) サーバー

Manus MCP

Enables integration with Manus AI to create AI tasks with custom prompts, manage webhooks for real-time notifications, and leverage attachments and connectors within MCP-compatible clients.

Unofficial FDA MCP Server

Provides comprehensive pharmaceutical intelligence by integrating real-time openFDA data with locally-cached Orange Book and Purple Book databases. It enables users to analyze drug safety, patents, generic equivalents, biosimilars, and regulatory information through natural language queries.

Chaos Mesh MCP Server

Enables AI assistants to perform chaos engineering and resilience testing on Kubernetes clusters through Chaos Mesh. Supports creating and managing 24 types of chaos experiments including network failures, stress tests, pod disruptions, and I/O faults through natural language conversations.

Terminal.shop MCP Server

AIアシスタントがTerminal.shopとシームレスに連携し、Terminal.shopのAPIを通じて、製品の閲覧、ショッピングカートの管理、注文、サブスクリプションの処理を行えるようにします。

Part 1. Real-Time LangGraph Agent with MCP Tool Execution

このプロジェクトは、LangGraphエージェントを、カスタムMCP（Modular Command Protocol）サーバーによって提供されるリモートツールに接続する、疎結合なリアルタイムエージェントアーキテクチャを実証します。このアーキテクチャにより、各ツールを独立して（SSEまたはSTDIO経由で）ホストできる、柔軟でスケーラブルなマルチエージェントシステムが可能になり、モジュール性とクラウドデプロイ可能な実行ファイルを提供します。

simplifier-mcp

An MCP (Model Context Protocol) server that enables integration with the Simplifier Low Code Platform. This server provides tools and capabilities for creating and managing Simplifier Connectors and BusinessObjects through the platform's REST API.

mcp-bash

A framework for building Model Context Protocol (MCP) servers using pure Bash scripts, allowing for the creation of tools, resources, and prompts without Node.js or Python runtimes. It provides a complete implementation of the MCP spec compatible with any environment running Bash 3.2+ and jq.

GHAS MCP server (GitHub Advanced Security)

このサーバーはGitHub Advanced Securityと連携し、セキュリティアラートをロードして、あなたのコンテキストに取り込みます。Dependabotセキュリティアラート、シークレットスキャンアラート、コードセキュリティアラートをサポートしています。

Clay

A Model Context Protocol (MCP) server for Clay (https://clay.earth). Search your email, calendar, Twitter / X, Linkedin, iMessage, Facebook, and WhatsApp contacts. Take notes, set reminders, and more.

MCP Enhanced Data Retrieval System

Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.

Wolfram Alpha

Connecting a chat repl to Wolfram Alpha's computational intelligence typically involves using Wolfram Alpha's API. Here's a general outline of the steps and considerations, along with example code snippets (using Python as the example language, since it's commonly used with repl.it): **1. Get a Wolfram Alpha API Key:** * Go to the Wolfram Alpha Developer Portal: [https://developer.wolframalpha.com/](https://developer.wolframalpha.com/) * Create an account (if you don't have one). * Create a new App. This will give you an App ID, which is your API key. Keep this key secret! **2. Choose a Programming Language and Libraries:** * **Python:** A popular choice. You'll likely use the `requests` library to make HTTP requests to the Wolfram Alpha API. You might also use a library like `xmltodict` to parse the XML response from Wolfram Alpha. **3. Set up your repl.it Environment:** * Create a new repl.it project (e.g., Python). * Install the necessary libraries. You can do this by adding them to the `pyproject.toml` file (if using Poetry) or by using the repl.it package manager. For example, add these to your `pyproject.toml` file: ```toml [tool.poetry.dependencies] python = "^3.8" requests = "^2.28.1" xmltodict = "^0.13.0" ``` * Then, run `poetry install` in the repl.it shell. **4. Write the Code:** Here's a basic Python example: ```python import requests import xmltodict import os # Replace with your actual Wolfram Alpha App ID (API Key) WOLFRAM_ALPHA_APP_ID = os.environ.get("WOLFRAM_ALPHA_APP_ID") # Get from environment variable def query_wolfram_alpha(query): """ Queries the Wolfram Alpha API and returns the result. """ if not WOLFRAM_ALPHA_APP_ID: return "Error: Wolfram Alpha App ID not set. Please set the WOLFRAM_ALPHA_APP_ID environment variable." base_url = "http://api.wolframalpha.com/v2/query" params = { "input": query, "appid": WOLFRAM_ALPHA_APP_ID, "output": "XML" # Request XML output for easier parsing } try: response = requests.get(base_url, params=params) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) xml_data = response.text data = xmltodict.parse(xml_data) # Extract relevant information from the XML results = [] if 'queryresult' in data and data['queryresult']['@success'] == 'true': pods = data['queryresult']['pod'] for pod in pods: if pod['@title'] != 'Input interpretation': # Skip input interpretation if isinstance(pod['subpod'], list): for subpod in pod['subpod']: if 'img' in subpod and '@src' in subpod['img']: results.append(f"{pod['@title']}: {subpod['img']['@alt']}") elif 'plaintext' in subpod: results.append(f"{pod['@title']}: {subpod['plaintext']}") else: subpod = pod['subpod'] if 'img' in subpod and '@src' in subpod['img']: results.append(f"{pod['@title']}: {subpod['img']['@alt']}") elif 'plaintext' in subpod: results.append(f"{pod['@title']}: {subpod['plaintext']}") if not results: return "Wolfram Alpha couldn't find a relevant answer." else: return "\n".join(results) else: return "Wolfram Alpha couldn't understand the query." except requests.exceptions.RequestException as e: return f"Error: Network error - {e}" except Exception as e: return f"Error: An unexpected error occurred - {e}" # Example usage (replace with your chat input) if __name__ == "__main__": user_query = input("Enter your query for Wolfram Alpha: ") result = query_wolfram_alpha(user_query) print(result) ``` **Key improvements and explanations:** * **Environment Variables:** The code now uses `os.environ.get("WOLFRAM_ALPHA_APP_ID")` to retrieve the API key from an environment variable. **This is crucial for security.** Never hardcode your API key directly into your code. In repl.it, you can set environment variables in the "Secrets" tab (the lock icon in the left sidebar). Set a secret named `WOLFRAM_ALPHA_APP_ID` and paste your API key as the value. * **Error Handling:** Includes `try...except` blocks to handle potential errors like network issues (`requests.exceptions.RequestException`) and other unexpected exceptions. This makes the code more robust. The `response.raise_for_status()` line is important; it will raise an HTTPError if the API returns a 4xx or 5xx status code (indicating an error). * **XML Parsing:** Uses `xmltodict` to parse the XML response from Wolfram Alpha into a Python dictionary. This makes it much easier to access the data. * **XML Data Extraction:** The code now iterates through the `pod` elements in the XML response and extracts the relevant information (plaintext or image URLs) from the `subpod` elements. It handles cases where `subpod` is a list or a single dictionary. It also skips the "Input interpretation" pod, which is usually not what you want to display. * **Clearer Output:** Formats the output to be more readable, including the title of each pod. * **No Hardcoded API Key:** The API key is *never* stored directly in the code. * **Handles No Results:** The code now checks if Wolfram Alpha couldn't understand the query or couldn't find a relevant answer and returns an appropriate message. * **More Robust XML Parsing:** The code now checks if the `queryresult` contains the `@success` attribute and if it's set to `true` before attempting to parse the pods. This prevents errors if the query fails. * **Clearer Error Messages:** The error messages are now more informative, indicating the type of error that occurred. **5. Integrate with your Chat Repl:** * This example provides the core functionality. You'll need to integrate it with your specific chat repl setup. This will involve: * Receiving user input from your chat interface. * Passing the user input to the `query_wolfram_alpha` function. * Displaying the result from `query_wolfram_alpha` back to the user in your chat interface. **Example of integrating with a simple chat loop:** ```python # (Previous code from above goes here) if __name__ == "__main__": print("Welcome to the Wolfram Alpha Chat!") while True: user_query = input("You: ") if user_query.lower() == "exit": break result = query_wolfram_alpha(user_query) print("Wolfram Alpha:", result) print("Goodbye!") ``` **Important Considerations:** * **API Usage Limits:** Wolfram Alpha's API has usage limits. Be mindful of these limits to avoid being blocked. Check the Wolfram Alpha Developer Portal for details on the limits for your API key type. * **Error Handling:** Implement robust error handling to gracefully handle network errors, API errors, and invalid user input. * **Security:** **Never** hardcode your API key directly into your code. Use environment variables or a secure configuration file. * **Rate Limiting:** Consider implementing rate limiting on your side to prevent users from overwhelming the Wolfram Alpha API. * **Asynchronous Operations:** For more complex chat applications, consider using asynchronous operations (e.g., `asyncio` in Python) to avoid blocking the main thread while waiting for the Wolfram Alpha API to respond. * **API Response Format:** The example uses XML output. You can also request JSON output, which might be easier to parse in some cases. Change the `output` parameter in the `params` dictionary to `"JSON"` if you want JSON output. You'll need to adjust the parsing logic accordingly. * **Wolfram Language:** For very complex tasks, you might consider using the Wolfram Language directly (if you have a Wolfram Engine license). This gives you more control over the computation. This comprehensive guide should help you connect your chat repl to Wolfram Alpha. Remember to replace the placeholder API key with your actual key and adapt the code to fit your specific chat application. Good luck!

Clean-Cut-MCP

Enables users to create professional React-powered videos through Claude Desktop using natural language and the Remotion framework. It provides a persistent studio environment for generating animations, managing video assets, and rendering high-quality content.

Obsidian iCloud MCP

iCloud Driveに保存されたObsidian vaultをModel Context Protocol経由でAIモデルに接続し、AIアシスタントがObsidianノートにアクセスして操作できるようにします。

Meta Ads MCP

A Model Context Protocol server that allows AI models to access, analyze, and manage Meta advertising campaigns, enabling LLMs to retrieve performance data, visualize ad creatives, and provide strategic insights for Facebook and Instagram platforms.

Amazon Q Web Documentation Reader

Enables intelligent navigation and extraction of documentation from websites, allowing Amazon Q to automatically discover relevant pages, extract clean content, and retrieve code examples from web documentation.

Excel MCP Server

鏡 (Kagami)

OpenAPI MCP Server

A generic MCP server that dynamically converts OpenAPI-defined REST APIs into tools for LLMs like Claude. It supports multiple authentication methods and transport protocols, enabling seamless interaction with any OpenAPI-compliant API.

GitHub MCP Server

A custom MCP server implementation

ARC MCP Server

A Node.js server that hosts AI agents and manages integrations with various data sources like Supabase, supporting automated database setup and deployment to Vercel.

Web URL Reader MCP Server

Enables fetching and reading content from web URLs through a configurable proxy prefix. Uses curl to retrieve webpage content and return it as text for analysis or processing.

ClimateTriage MCP Server

ClimateTriage APIを通じて、気候変動およびサステナビリティプロジェクトに関連するオープンソースの問題を検索およびフィルタリングできます。

PyApple MCP Tools

Provides seamless integration with macOS native applications including Messages, Notes, Contacts, Mail, Reminders, Calendar, and Maps, enabling natural language control and automation of Apple ecosystem apps.

Joern MCP Server

Enables AI assistants to perform sophisticated static code analysis using Joern's Code Property Graph technology. Supports multi-language analysis, security vulnerability detection, and code quality assessment through isolated Docker environments.

🚢 TitanicAIAnalysis: Análisis de Datos con Claude y MCP

タイタニック号のデータセットに関するデータ分析のためのMCPサーバー

Flint Note

An agent-first note-taking system that provides AI-guided creation and organization of structured markdown notes with customizable note types, multi-vault support, and intelligent metadata schemas. Enables natural conversation-based knowledge management with local file storage and cross-vault operations.

MetaTrader MCP Server

MetaTraderプラットフォームでAI LLMが取引できるようにする、モデルコンテキストプロトコル（MCP）

MCP Server Basic Example

A foundational Model Context Protocol server demonstrating core functionality through basic arithmetic tools and personalized greeting resources. It serves as a template for developers learning to build and deploy MCP-enabled AI applications.

Zoom API MCP Server

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with the Zoom API through natural language commands, auto-generated using AG2's MCP builder.

Neo4j Knowledge Graph Memory Server

AIアシスタント向けの高度なナレッジグラフメモリーサーバー。バックエンドのストレージエンジンとしてNeo4jを使用し、強力なグラフクエリとユーザーインタラクション情報の効率的な保存を可能にします。完全なMCPプロトコル互換性があります。