Discover Awesome MCP Servers

MCP JSON Server

Enables comprehensive JSON file operations including reading, writing, transforming, and analyzing JSON data from local files and URLs. Supports JSON pointer access, schema inference, data merging, and specialized market data optimization tools.

News Instagram MCP Server

An intelligent system that automatically scrapes Canadian news sources, processes content with AI, and publishes engaging Instagram posts.

MySQL MCP Server

A Message Control Protocol server that provides an interface for managing and querying departmental budget information through a MySQL database.

example-mcp-server-streamable-http

example-mcp-server-streamable-http

gloria-mcp

MCP server for Gloria AI curated crypto news. Provides curated, real-time cryptocurrency news digests, recaps, and search.

Search Service

Search MCP Serverは、Claude DesktopやCursorのようなツールにおいて、ネットワーク検索とローカル検索機能をシームレスに統合することを可能にします。Brave Search APIを利用することで、高並行性と非同期リクエストを実現しています。

Figma MCP PRO

Professional Model Context Protocol server that enables AI-optimized Figma design analysis and comprehensive design-to-code conversion through a structured 5-step workflow.

Medium MCP Server

Enables AI assistants to interact with Medium's platform for publishing, updating, and managing articles and drafts through OAuth 2.0 authentication with automatic retry logic and rate limit handling.

DNDzgz MCP Server

An MCP server that provides real-time information about the Zaragoza tram system, including arrival estimations and station details through the DNDzgz API.

Trilogy AI MCP Server

An MCP server that analyzes Trilogy AI Center of Excellence Substack publications, allowing users to list, search, and analyze trends in the AI content.

tui-mcp

What Chrome DevTools MCP is for the browser, tui-mcp is for the terminal. Launch any TUI app, take screenshots, send keystrokes, read text - works with any framework.

WSB Analyst MCP Server

A Model Context Protocol server that enables LLM clients to fetch, analyze, and extract insights from real-time WallStreetBets posts, comments, and shared links for market analysis.

Elysia MCP Starter

A template for building Model Context Protocol servers using Elysia and Bun runtime, enabling LLM clients like Claude Desktop and Cody to access custom tools, prompts, and data resources.

Cloudflare Playwright MCP

Enables AI assistants to control a browser through Playwright on Cloudflare Workers, allowing them to perform web automation tasks like navigation, typing, clicking, and taking screenshots.

Minecraft Survival MCP Server

Enables LLM agents to play and survive in Minecraft by abstracting low-level tasks like pathfinding, building, and crafting into high-level transactional commands. It utilizes a "Helix" architecture to handle execution and coordinate math autonomously, allowing models to focus on strategy and high-level intent.

mcp-hilton

An MCP server that enables AI agents to search Hilton hotels, manage reservations, and check Hilton Honors status through browser automation. It supports the full booking flow, points redemption, and digital key access via natural language commands.

MCP SQL Server

An MCP server for Microsoft SQL Server integration that enables users to query, monitor, and analyze databases directly through Claude. It supports schema exploration, performance analysis, and optional write operations via natural language commands.

JSer.info MCP Server

A Model Context Protocol server that provides search and retrieval capabilities for JSer.info's JavaScript resource database, enabling access to items, posts, product information, and timeline data through various specialized tools.

Dental Clinic Loan Verification MCP Server

Enables automated dental clinic loan verification by combining rule-based ID validation with LLM-powered document analysis and fraud detection. It supports provider-agnostic vision and reasoning tools to assess document consistency, verify credentials like PAN and GST, and generate comprehensive risk narratives.

Plane MCP Server

A Model Context Protocol (MCP) server that enables LLMs to interact with Plane.so, allowing them to manage projects and issues through Plane's API. Using this server, LLMs like Claude can directly interact with your project management workflows while maintaining user control and security.

MCP

Okay, I understand. You want to set up your MCP (presumably a custom application or system) to display company information and stock prices, leveraging the Claude model (likely for natural language processing or data retrieval). Here's a breakdown of the steps and considerations, along with a conceptual outline. Keep in mind that the specific implementation will depend heavily on the architecture of your MCP system and the available APIs for Claude and financial data. **Conceptual Outline** 1. **Data Sources:** * **Company Information:** Identify a reliable source of company information. This could be a database you maintain, a commercial API (e.g., Crunchbase, Clearbit), or a web scraping solution (less reliable but sometimes necessary). * **Stock Prices:** Choose a stock market data provider. Popular options include: * **IEX Cloud:** Relatively affordable and easy to use. * **Alpha Vantage:** Offers a free tier with limitations. * **Financial Modeling Prep:** Another option with various data points. * **Refinitiv (formerly Thomson Reuters):** More expensive but provides comprehensive data. * **Bloomberg:** High-end, professional-grade data. * **Claude API:** You'll need access to the Claude API. This usually involves signing up for an account and obtaining an API key. 2. **MCP System Integration:** * **API Endpoints:** Design API endpoints within your MCP system to handle requests for company information and stock prices. For example: * `/company/{company_name}`: Returns company details. * `/stock/{ticker_symbol}`: Returns stock price information. * **Data Storage (Optional):** Consider caching frequently accessed data in a database within your MCP system to reduce API calls to external services and improve response times. 3. **Claude Integration:** * **Natural Language Understanding (NLU):** Use Claude to understand user queries. For example, if a user types "Tell me about Apple's stock," Claude should identify the intent ("get stock information") and the entity ("Apple"). * **Data Retrieval:** Based on the NLU results, construct the appropriate API calls to your data sources (company information and stock price APIs). * **Response Formatting:** Use Claude to format the retrieved data into a human-readable response. This is where Claude's natural language generation (NLG) capabilities shine. 4. **Error Handling:** * Implement robust error handling to gracefully handle API failures, invalid user input, and other potential issues. **Detailed Steps and Considerations** 1. **Choose Your Data Sources:** * **Company Information:** * **API:** If you choose an API, review its documentation to understand how to query for company information (e.g., by name, ticker symbol, or domain). Consider the API's rate limits and pricing. * **Database:** If you have a database, ensure it's well-structured and indexed for efficient querying. * **Web Scraping:** Use a library like Beautiful Soup (Python) or Cheerio (Node.js) to scrape data from websites. Be aware of the legal and ethical implications of web scraping. Websites can change their structure, breaking your scraper. * **Stock Prices:** * **API:** Select a stock market data API based on your budget and data requirements. Pay attention to: * **Real-time vs. Delayed Data:** Real-time data is more expensive. Delayed data (e.g., 15-minute delay) may be sufficient for some use cases. * **Historical Data:** If you need historical stock prices, ensure the API provides it. * **API Limits:** Understand the API's rate limits and usage restrictions. 2. **Set Up Your MCP API Endpoints:** * Use a framework like Flask (Python), Express.js (Node.js), or Spring Boot (Java) to create your API endpoints. * Implement authentication and authorization if necessary to protect your API. * Use a consistent API design (e.g., RESTful principles). 3. **Integrate with Claude:** * **Install the Claude API client:** Follow the instructions in the Claude API documentation to install the appropriate client library for your programming language. * **Authentication:** Use your Claude API key to authenticate your requests. * **NLU (Natural Language Understanding):** * Send the user's query to the Claude API. * Use Claude's NLU capabilities to extract the intent and entities. You might need to fine-tune Claude for your specific use case. Consider using prompt engineering to guide Claude's understanding. For example: ```python import anthropic client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_API_KEY") def get_intent_and_entities(query): prompt = f"""You are a helpful assistant that analyzes user queries to determine their intent and extract relevant entities. User Query: {query} Intent: (The user's goal, e.g., "get stock information", "get company details") Entities: (A list of relevant entities, e.g., "Apple", "AAPL") """ response = client.completions.create( model="claude-v1.3", # Or the latest Claude model prompt=prompt, max_tokens_to_sample=200, ) # Parse the response to extract the intent and entities. This will require some string manipulation. # You might want to use regular expressions to make this easier. # Example: # intent = response.completion.split("Intent: ")[1].split("\n")[0].strip() # entities = response.completion.split("Entities: ")[1].split("\n")[0].strip().split(", ") # Return the intent and entities return intent, entities ``` * **Data Retrieval:** * Based on the extracted intent and entities, construct the appropriate API calls to your data sources. * Handle potential errors from the data source APIs. * **Response Formatting (NLG - Natural Language Generation):** * Use Claude to format the retrieved data into a human-readable response. Again, prompt engineering is crucial here. For example: ```python def format_response(company_name, stock_price, company_description): prompt = f"""You are a helpful assistant that formats company information and stock prices into a human-readable response. Company Name: {company_name} Stock Price: {stock_price} Company Description: {company_description} Response: """ response = client.completions.create( model="claude-v1.3", # Or the latest Claude model prompt=prompt, max_tokens_to_sample=300, ) return response.completion ``` 4. **Error Handling:** * Implement `try...except` blocks (Python) or similar error handling mechanisms in your code. * Log errors to a file or monitoring system for debugging. * Return informative error messages to the user. **Example Code Snippet (Conceptual - Python with Flask)** ```python from flask import Flask, request, jsonify import anthropic import requests # For making API calls to data sources app = Flask(__name__) # Replace with your actual API keys and data source URLs CLAUDE_API_KEY = "YOUR_CLAUDE_API_KEY" STOCK_API_URL = "https://api.example.com/stock/{ticker}" # Example COMPANY_API_URL = "https://api.example.com/company/{name}" # Example client = anthropic.Anthropic(api_key=CLAUDE_API_KEY) def get_intent_and_entities(query): # (Implementation as described above) # This is a placeholder. You'll need to parse the Claude response. if "stock" in query.lower(): intent = "get stock information" entity = query.split("stock of ")[-1].strip() # Very basic example elif "company" in query.lower(): intent = "get company information" entity = query.split("about ")[-1].strip() # Very basic example else: intent = "unknown" entity = None return intent, entity def get_stock_price(ticker): try: response = requests.get(STOCK_API_URL.format(ticker=ticker)) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() return data["price"] except requests.exceptions.RequestException as e: print(f"Error fetching stock price: {e}") return None def get_company_description(company_name): try: response = requests.get(COMPANY_API_URL.format(name=company_name)) response.raise_for_status() data = response.json() return data["description"] except requests.exceptions.RequestException as e: print(f"Error fetching company description: {e}") return None def format_response(company_name, stock_price, company_description): # (Implementation as described above) prompt = f"""You are a helpful assistant that formats company information and stock prices into a human-readable response. Company Name: {company_name} Stock Price: {stock_price} Company Description: {company_description} Response: """ response = client.completions.create( model="claude-v1.3", # Or the latest Claude model prompt=prompt, max_tokens_to_sample=300, ) return response.completion @app.route("/query", methods=["POST"]) def handle_query(): query = request.json.get("query") if not query: return jsonify({"error": "Missing query parameter"}), 400 intent, entity = get_intent_and_entities(query) if intent == "get stock information": stock_price = get_stock_price(entity) if stock_price is None: return jsonify({"error": "Could not retrieve stock price"}), 500 company_description = get_company_description(entity) # Try to get company description too if company_description is None: company_description = "No description available." formatted_response = format_response(entity, stock_price, company_description) return jsonify({"response": formatted_response}) elif intent == "get company information": company_description = get_company_description(entity) if company_description is None: return jsonify({"error": "Could not retrieve company information"}), 500 formatted_response = format_response(entity, "N/A", company_description) # Stock price not applicable return jsonify({"response": formatted_response}) else: return jsonify({"error": "Unknown intent"}), 400 if __name__ == "__main__": app.run(debug=True) ``` **Key Improvements and Considerations:** * **Prompt Engineering:** The quality of Claude's responses depends heavily on the prompts you provide. Experiment with different prompts to optimize the results. Consider using few-shot learning (providing examples in the prompt) to guide Claude's behavior. * **Error Handling:** The example code includes basic error handling, but you should implement more robust error handling in a production environment. * **API Rate Limits:** Be mindful of the API rate limits of your data sources and the Claude API. Implement caching and rate limiting in your MCP system to avoid exceeding these limits. * **Security:** Protect your API keys and other sensitive information. Use environment variables to store API keys and avoid hardcoding them in your code. * **Scalability:** Consider the scalability of your MCP system. If you expect a large number of requests, you may need to use a load balancer and multiple servers. * **Testing:** Thoroughly test your MCP system to ensure it is working correctly. Write unit tests and integration tests to verify the functionality of your code. * **Monitoring:** Monitor your MCP system to identify and resolve issues quickly. Use a monitoring tool to track API response times, error rates, and other metrics. * **Data Validation:** Validate the data returned from the APIs to ensure it is accurate and consistent. * **Asynchronous Operations:** For long-running tasks (like calling external APIs), consider using asynchronous operations (e.g., with `asyncio` in Python) to improve the responsiveness of your MCP system. * **User Interface:** Consider how users will interact with your MCP system. You may want to create a web interface or a command-line interface. **Summary** This is a complex project that requires careful planning and execution. Start with a simple prototype and gradually add more features. Focus on the core functionality first (data retrieval and response formatting) and then add more advanced features like error handling, caching, and rate limiting. Remember to consult the documentation for the Claude API and your chosen data source APIs. Good luck!

FastAPI MCP Server

Wraps FastAPI REST endpoints as MCP tools, enabling natural language interaction with user management, task management, and mathematical calculations through Gemini CLI.

COTI MCP Server

Enables AI applications to interact with the COTI blockchain for private token operations, including deployment and management of private ERC20 tokens and ERC721 NFTs using COTI's Multi-Party Computation (MPC) technology.

Apple Find My MCP Server

Enables interaction with Apple's Find My network to track devices, check battery status, and manage device information. Provides secure authentication and caching for efficient access to your Apple devices' location and status data.

Entscheidsuche MCP Server

Provides access to Swiss court decisions through the entscheidsuche.ch API, enabling search, retrieval, and analysis of legal documents across different cantons and courts using natural language queries.

Claude Mobile

Enables mobile device automation for Android (via ADB) and iOS Simulator (via simctl), allowing you to control devices with natural language through screenshots, UI interactions, text input, and app management.

qontoctl

MCP server for Qonto

mcp-test-server

A lightweight MCP test server for verifying client connectivity, providing tools, resources, and prompts for integration.

MCP Server

A server implementing Model Coupling Protocol for HDF5 file operations, Slurm job management, hardware monitoring, and data compression.