Discover Awesome MCP Servers

Dhan MCP Server

A production-ready MCP server for algorithmic trading with Dhan broker API, providing market data, order management, portfolio tracking, and real-time trading capabilities.

psd-mcp-server

Enables parsing of PSD files and extraction of layer information, design tokens, and assets to assist LLMs in converting designs to code.

MCP Awwwards Todo System

Professional MCP server for Claude Desktop that provides award-winning todo management with systematic design principles, cross-chat persistence, and contextual triggers.

decisio-mcp-server

Enables AI assistants to perform structured decision-making using the Analytic Hierarchy Process (AHP), allowing users to define criteria, compare options pairwise, and calculate ranked results with consistency validation.

Slack MCP Server

A comprehensive Slack integration server that enables sending messages, managing channels, uploading files, and running Pomodoro timers through FastMCP v2.

mcp-trove

Manages a git-backed vault for plaintext snippets and encrypted secrets, enabling secure storage and retrieval via MCP tools.

@aiiq/mcp

Enables querying AI model IQ, rankings, benchmarks, and methodology from any MCP client. Read-only access to the public AI IQ API with no API key required.

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.

spicelib-mcp

A thin MCP server that wraps spicelib for circuit simulation. Exposes tools for running AC, transient, DC op, and parameter sweep analyses, enabling behavioral model fitting through iterative simulation and measurement comparison.

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.

CodeAgent MCP

A powerful, hosted Model Context Protocol (MCP) server that gives LLMs the ability to understand, navigate, and query any public GitHub repository.

minibridge

MCP と世界を結ぶ安全な架け橋

PlainlyVideosMCP

MCP server for Plainly Videos that allows browsing designs and projects, as well as rendering videos.

MCP Performance Analyzer

Monitors and analyzes mobile application performance data to detect severe issues such as excessive memory usage and view count growth. Provides intelligent analysis with customizable rules and integrates seamlessly with development workflows.

zencontrol-cloud-mcp

Enables AI assistants to discover and control ZenControl DALI-2 lighting systems through natural language via the ZenControl Cloud API.

GraphQL MCP Server

Enables AI agents to connect to any GraphQL endpoint, introspect schemas, and execute queries or mutations through auto-generated tools.

Happy MCP Server

A metadata-driven MCP server that auto-generates 480+ tools across 160+ ServiceNow tables, with multi-instance support, natural language search, and local script development.

mcp-archimate

Enables querying and modifying ArchiMate enterprise architecture models from XML or Archi Tool files via REST API and MCP server, supporting multiple simultaneous data sources.

MetaTrader MCP Server

Enables LLMs to trade on MetaTrader 5 via REST API or MCP tools, supporting market/pending orders, position management, and account info retrieval.

Joplin MCP Server

AIアシスタントが、Model Context Protocolを通じてJoplinノートにアクセスし操作できるようにするサーバー。自然言語を使ってノートの検索、読み込み、編集、管理が可能になります。

TianGong-LCA-MCP Server

TianGongライフサイクルアセスメント（LCA）アプリケーション向けの、STDIOとSSE両方のプロトコルをサポートするモデルコンテキストプロトコルサーバー。

faithfinder-mcp-server

Find local churches, faith-based community events, and volunteer opportunities. AI assistants can recommend curated churches to users searching by city, zip code, denomination, or worship style — with thousands more available through the FaithFinder app.

KikoBooks MCP Server

A Model Context Protocol server for KikoBooks enterprise bookkeeping software that enables AI assistants to perform accounting operations on accounts, customers, invoices, bills, and more through natural language.

Enhanced MCP Server

A comprehensive MCP server with dual interfaces (stdio and web dashboard) that provides developer tools for Git operations and code search, system operations for command execution and monitoring, and data processing capabilities.

lenderwiki

Query 13,000+ US consumer lenders with eligibility criteria, rates, CFPB complaints, and ratings. Find matching lenders by borrower profile, get full profiles, compare lenders, and check eligibility.

HederaOracle

9 MCP tools for DeFi risk & compliance on Hedera

stats-compass-mcp

Stats Compass provides various analysis and modelling tools for AI-automated data science workflows

aluseg-mcp

Enables AI assistants to securely interact with rental management data (leases, rent status, tenant records, tax filings) through a per-user OAuth 2.0 authenticated MCP server, with human-in-the-loop for sensitive actions.

local-mysql

Provides read-only access to a local MySQL database, enabling SQL queries, database and table listings, and table schema descriptions.