Discover Awesome MCP Servers

Real Estate Investment MCP Server

An MCP server that connects to a SQLite database of Zillow real estate data, enabling users to explore property values, rent indexes, and forecasts through a chat interface to make informed investment decisions.

Agent Care

Um servidor MCP que fornece ferramentas de saúde para interagir com dados FHIR e recursos médicos em prontuários eletrônicos (EMR) como Cerner e Epic.

MCP Code Checker

Servidor MCP que fornece verificações de qualidade de código (pylint e pytest) com prompts inteligentes e amigáveis para LLMs (Modelos de Linguagem Grandes) para análise e correções. Permite que Claude e outros assistentes de IA analisem seu código e sugiram melhorias.

EVM MCP Server

Espelho de

MIRO Whiteboard Connector

Um servidor de Protocolo de Contexto de Modelo para conectar-se ao Aplicativo de Quadro Branco MIRO. Permite manipulação de quadro, criação de notas adesivas, operações em massa e muito mais.

Kubernetes MCP Server

A Model Control Protocol server that extends AI assistants with Kubernetes operations capabilities, allowing for managing deployments, pods, services and other K8s resources.

Mcp Assignment

Testar seus servidores MCP.

Playwright MCP Server

A Model Context Protocol server that provides browser automation capabilities using Playwright, enabling LLMs to interact with web pages, take screenshots, generate test code, scrape web content, and execute JavaScript in real browser environments.

File Operation MCP Server

Enables comprehensive file and document operations including image compression, archive creation/extraction, file copying/moving, PDF merging/splitting/conversion, SQLite database queries, and advanced text processing.

Postman MCP Generator

Automatically converts Postman API collections into MCP-compatible tools for AI assistants. Enables users to interact with any API through natural language by generating JavaScript tools from Postman requests.

Overleaf MCP Server

Enables read-only interaction with Overleaf LaTeX projects through compatible clients like Claude Desktop, Cursor, and VS Code. Allows users to list and read project files safely without modification capabilities.

Roblox Studio MCP Server

An AI-powered server that provides access to Roblox Studio data through a plugin architecture, enabling AI tools to interact with file systems, studio context, properties, and project structure.

svelte-llm

Svelte and SvelteKit developer odcumentation

MCP Todo.txt Integration

A server implementation that enables LLMs to programmatically manage tasks in Todo.txt files using the Model Context Protocol (MCP), supporting operations like adding, completing, deleting, listing, searching, and filtering tasks.

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.

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, let's break down how you can configure an MCP (presumably, you mean a **Monitoring and Control Platform** or a similar system) to view company information and stock prices using Claude (likely referring to the **Anthropic Claude AI model**). This involves several steps and considerations. I'll outline a general approach, and you'll need to adapt it to your specific MCP and its capabilities. **High-Level Overview** The core idea is to: 1. **Gather Data:** Get company information and stock prices from reliable sources (APIs, databases, etc.). 2. **Send Data to Claude:** Format the data and send it to the Claude API with a clear prompt. 3. **Receive and Parse Claude's Response:** Claude will analyze the data and provide insights. You need to parse this response. 4. **Display in MCP:** Integrate the parsed information into your MCP's interface. **Detailed Steps** **1. Data Acquisition** * **Company Information:** * **APIs:** Use APIs like the Crunchbase API, Clearbit API, or similar services that provide company profiles, funding information, employee counts, industry, etc. These usually require an API key and have usage limits. * **Databases:** If your company has its own database of company information, you can directly query that. * **Web Scraping (Use with Caution):** As a last resort, you *could* scrape websites like LinkedIn or company websites. However, web scraping is fragile (websites change), and you need to respect robots.txt and terms of service. It's generally better to use APIs. * **Stock Prices:** * **Financial APIs:** Use APIs like Alpha Vantage, IEX Cloud, Finnhub, or Yahoo Finance API (though Yahoo Finance's API is less reliable than it used to be). These provide real-time or near real-time stock prices, historical data, and other financial metrics. Again, you'll need an API key. * **Data Providers:** Consider professional data providers like Refinitiv or Bloomberg if you need very high-quality, low-latency data, but these are significantly more expensive. **2. Data Preparation and Formatting** * **Data Cleaning:** Clean the data you retrieve. Handle missing values, inconsistencies, and errors. * **Data Aggregation:** Combine the company information and stock price data into a single data structure (e.g., a Python dictionary or a JSON object) for each company you want to analyze. * **Prompt Engineering:** This is crucial for getting useful results from Claude. Craft a clear and specific prompt that tells Claude what you want it to do. Here's an example: ```python prompt = f""" Analyze the following information about {company_name}: Company Description: {company_description} Industry: {company_industry} Employee Count: {company_employee_count} Current Stock Price: {stock_price} Previous Day's Closing Price: {previous_close} Provide a brief summary of the company, its current financial situation based on the stock price, and potential risks or opportunities. Focus on key insights that would be relevant to a business analyst. Keep the response concise (under 200 words). """ ``` * **Variables:** Replace the placeholders (e.g., `{company_name}`, `{stock_price}`) with the actual data you've collected. * **Instructions:** Clearly tell Claude what you want it to do (summarize, analyze, identify risks, etc.). * **Context:** Provide enough context for Claude to understand the data. * **Constraints:** Set limits on the response length to avoid overly verbose outputs. * **Role:** You can even specify a role for Claude to play, such as "Act as a financial analyst..." **3. Sending Data to Claude (Using the API)** * **Install the Anthropic Python Library:** ```bash pip install anthropic ``` * **API Key:** You'll need an API key from Anthropic. Get one from their website after signing up for an account. * **Code Example (Python):** ```python import anthropic import os # Replace with your actual API key ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY") # Best practice: store API key in an environment variable client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY) def get_claude_response(prompt): try: completion = client.completions.create( model="claude-v1.3", # Or a newer Claude model prompt=f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}", max_tokens_to_sample=500, # Adjust as needed ) return completion.completion except Exception as e: print(f"Error calling Claude API: {e}") return None # Example usage: company_name = "Apple Inc." company_description = "Designs, develops, and sells consumer electronics, computer software, and online services." company_industry = "Technology" company_employee_count = 164000 stock_price = 175.00 previous_close = 173.50 prompt = f""" Analyze the following information about {company_name}: Company Description: {company_description} Industry: {company_industry} Employee Count: {company_employee_count} Current Stock Price: {stock_price} Previous Day's Closing Price: {previous_close} Provide a brief summary of the company, its current financial situation based on the stock price, and potential risks or opportunities. Focus on key insights that would be relevant to a business analyst. Keep the response concise (under 200 words). """ claude_response = get_claude_response(prompt) if claude_response: print(f"Claude's Analysis for {company_name}:\n{claude_response}") else: print("Failed to get a response from Claude.") ``` * **`anthropic.HUMAN_PROMPT` and `anthropic.AI_PROMPT`:** These are special tokens that help Claude understand the structure of the conversation. Always include them. * **`model`:** Specify the Claude model you want to use (e.g., "claude-v1.3", "claude-v2"). Check the Anthropic documentation for the latest models. * **`max_tokens_to_sample`:** Controls the maximum length of Claude's response. Adjust this based on your needs. * **Error Handling:** Include `try...except` blocks to handle potential errors when calling the API. **4. Parsing Claude's Response** * **Text Extraction:** The `completion.completion` attribute contains the text response from Claude. * **Structured Data (Optional):** If you want Claude to return structured data (e.g., JSON), you can modify your prompt to request it. However, Claude is not always perfect at generating valid JSON, so you might need to use regular expressions or other parsing techniques to extract the data reliably. For example: ```python prompt = f""" Analyze the following information about {company_name}: Company Description: {company_description} Industry: {company_industry} Employee Count: {company_employee_count} Current Stock Price: {stock_price} Previous Day's Closing Price: {previous_close} Provide a JSON object with the following keys: - summary: A brief summary of the company. - financial_outlook: A brief assessment of the company's financial situation. - risks: Potential risks. - opportunities: Potential opportunities. Example JSON: {{ "summary": "...", "financial_outlook": "...", "risks": "...", "opportunities": "..." }} """ ``` Then, you would try to parse the `claude_response` as JSON: ```python import json try: data = json.loads(claude_response) print(f"Summary: {data['summary']}") print(f"Financial Outlook: {data['financial_outlook']}") except json.JSONDecodeError: print("Could not parse Claude's response as JSON.") # Handle the error (e.g., use a regular expression to extract the data) ``` **5. Integration with Your MCP** * **MCP API/SDK:** Your MCP likely has an API or SDK that allows you to integrate external data sources. Consult your MCP's documentation for details. * **Data Visualization:** Use your MCP's charting and visualization tools to display the company information, stock prices, and Claude's analysis. * **Alerting:** Configure alerts based on stock price changes or specific insights from Claude (e.g., "Alert if Claude identifies a significant risk for Apple"). **Example Architecture** ``` [Data Sources (APIs, Databases)] --> [Data Aggregation & Formatting (Python Script)] --> [Claude API] --> [Response Parsing (Python Script)] --> [MCP API] --> [MCP Dashboard] ``` **Important Considerations** * **API Costs:** Be aware of the costs associated with using APIs like the Anthropic API and financial data APIs. Monitor your usage and set limits to avoid unexpected charges. * **Rate Limiting:** APIs often have rate limits (e.g., a maximum number of requests per minute). Implement error handling and retry mechanisms to deal with rate limiting. * **Data Accuracy:** The accuracy of Claude's analysis depends on the quality of the data you provide. Verify the data from your sources. * **Security:** Protect your API keys and other sensitive information. Store them securely (e.g., in environment variables) and avoid hardcoding them in your code. * **Prompt Engineering is Key:** Experiment with different prompts to get the best results from Claude. Iterate and refine your prompts based on the responses you receive. * **Claude's Limitations:** Claude is a powerful language model, but it's not perfect. It can sometimes make mistakes or provide inaccurate information. Always critically evaluate its output. * **Legal and Ethical Considerations:** Be mindful of data privacy regulations and ethical considerations when using company information and stock prices. Ensure you have the right to use the data and that you are not violating any laws or regulations. **Translation to Portuguese (of the key concepts):** * **Monitoring and Control Platform:** Plataforma de Monitoramento e Controle * **Anthropic Claude AI model:** Modelo de IA Claude da Anthropic * **API:** API (Interface de Programação de Aplicações) * **API Key:** Chave de API * **Prompt Engineering:** Engenharia de Prompt (ou Criação de Instruções) * **Data Cleaning:** Limpeza de Dados * **Data Aggregation:** Agregação de Dados * **Rate Limiting:** Limitação de Taxa * **Data Visualization:** Visualização de Dados * **Alerting:** Alertas **Example Portuguese Prompt:** ``` Analise as seguintes informações sobre a [Nome da Empresa]: Descrição da Empresa: [Descrição da Empresa] Indústria: [Indústria] Número de Funcionários: [Número de Funcionários] Preço Atual das Ações: [Preço Atual das Ações] Preço de Fechamento do Dia Anterior: [Preço de Fechamento do Dia Anterior] Forneça um breve resumo da empresa, sua situação financeira atual com base no preço das ações e potenciais riscos ou oportunidades. Concentre-se em insights importantes que seriam relevantes para um analista de negócios. Mantenha a resposta concisa (abaixo de 200 palavras). ``` **In summary, this is a complex integration that requires careful planning, coding, and testing. Start with a small, well-defined use case and gradually expand as you gain experience.** Remember to consult the documentation for your MCP and the Anthropic Claude API for the most up-to-date information. Good luck!

MySQL Database Access Server

Um servidor de Protocolo de Contexto de Modelo que fornece acesso somente leitura a bancos de dados MySQL, permitindo que LLMs (Modelos de Linguagem Grandes) inspecionem esquemas de banco de dados e executem consultas somente leitura.

Google Patents MCP Server

Um servidor de Protocolo de Contexto de Modelo que permite pesquisar informações do Google Patents através da API Google Patents da SerpApi, permitindo que os usuários consultem dados de patentes com vários filtros e opções de ordenação.

TypeScript MCP Server Boilerplate

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK. Includes example tools like calculator and greeting functions, plus system information resources.

Linkedin-Profile-Analyzer

Um poderoso analisador de perfis do LinkedIn que se integra perfeitamente com o Claude AI para buscar e analisar perfis públicos do LinkedIn, permitindo que os usuários extraiam, pesquisem e analisem dados de postagens por meio da API de Dados do LinkedIn da RapidAPI.

Postgres MCP Server

Enables comprehensive PostgreSQL database management through natural language including queries, schema operations, user management, and administrative tasks. Features enterprise-grade connection pooling, transaction support, and full database administration capabilities.

Tavily Web Search MCP Server

Enables web search capabilities through the Tavily API via the Model Context Protocol. Allows users to perform web searches and retrieve information from the internet through natural language queries.

MCP Knowledge Base

A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.

Chrome DevTools MCP

Enables AI coding assistants to control and inspect a live Chrome browser through DevTools for automated testing, performance analysis, debugging, and web scraping. Provides reliable browser automation using Puppeteer with comprehensive DevTools access.

MCP + DeepSeek AI Integration Server

A Node.js-based FastMCP protocol server that integrates DeepSeek AI capabilities for intelligent conversations and code analysis, providing tool invocation abilities through the MCP protocol.

Karakeep MCP server

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my-docs-mcp-server

Um servidor MCP leve que indexa e pesquisa arquivos Markdown em um diretório especificado, fornecendo pesquisa de texto completo e recuperação de arquivos através do Protocolo de Contexto de Modelo.

Listmonk MCP Server

O servidor Listmonk-MCP é usado como um servidor MCP para utilizar o seu servidor Listmonk.

StatPearls MCP Server

Provides AI systems with access to peer-reviewed medical information from StatPearls, enabling searches for diseases and medical conditions with comprehensive, reliable content formatted in AI-friendly Markdown.