Discover Awesome MCP Servers

Slack MCP Server

Enables complete Slack integration through natural language in Cursor IDE, supporting message sending, channel management, direct messaging, user lookup, reactions, and message search using the Slack API.

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.

lol-client-mcp Public

Um servidor MCP (Modelo-Controlador-Processador) para acessar dados do cliente de League of Legends. Este servidor fornece uma coleção de ferramentas que se comunicam com a API de Dados do Cliente Ativo de League of Legends para recuperar dados do jogo.

MCP Server Boilerplate

A starter template for building MCP (Model Context Protocol) servers that can integrate with Claude, Cursor, or other MCP-compatible AI assistants. Provides a foundation with example implementations of tools, resources, and prompts along with installation scripts.

Boilerplate MCP

A template repository for developing Model Context Protocol (MCP) servers with devcontainer setup and curated learning resources.

Xueqiu MCP

Um serviço MCP baseado na API do Xueqiu (mercado de ações chinês) que permite aos usuários consultar dados de ações diretamente através do Claude ou outros assistentes de IA.

KHU Notebook Research Assistant

An MCP server that interfaces with Google NotebookLM to enable autonomous academic research and systematic knowledge management. It allows users to perform deep web searches and automatically generate study artifacts like research reports, presentation slides, and audio overviews.

Knowledge Graph Memory Server

A persistent memory system using a local knowledge graph that enables Claude to remember information about users across chats, with advanced search, graph traversal, and filtering capabilities for entities, relations, and observations.

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.

Azure Container Apps Weather MCP Server

A server-sent events (SSE) MCP server that runs on Azure Container Apps with API key authentication, likely providing weather-related functionality based on the configuration.

Seamless Sign-ups MCP Server

A demonstration project that uses Google Gemini 2.0 Flash to interact with a locally hosted Model Calling Protocol server for managing user registration data stored in CSV files.

ccontext

Provides AI agents with persistent execution context across sessions through local YAML files, enabling shared memory, task tracking, milestone management, and context hygiene for single or multi-agent workflows.

task

Um servidor MCP para expor um formato de dados para traduzir tarefas em ações ambientais.

Claude Data Buddy

Enables conversational analysis of CSV and Parquet files through natural language, providing statistics, summaries, data type information, and comprehensive multi-step data analysis.

Cloud IoT API MCP Server

A Multi-Agent Conversation Protocol server that provides an interface to Google's Cloud IoT API, allowing agents to interact with and manage IoT devices and registries through natural language.

MCP Store Greeting Server

Provides personalized store greetings combined with real-time weather information based on store location. Includes an admin web interface for managing stores with address autocomplete and map-based location selection.

TxtAI MCP Server

txtai é um banco de dados de embeddings completo para pesquisa semântica, orquestração de LLM e fluxos de trabalho de modelos de linguagem. Todas as funcionalidades podem ser servidas através de sua API e a API suporta MCP. Documentação: https://neuml.github.io/txtai/api/mcp/

BOJ MCP Server

Enables AI assistants to access solved.ac user profiles and search Baekjoon Online Judge problems by difficulty, tags, and keywords through the solved.ac API.

Railway MCP Server

Enables management of Railway.app infrastructure through natural language, including deploying services, managing environment variables, monitoring deployments, and controlling project resources.

filesystem-mcp

Um servidor MCP baseado em TypeScript que implementa um sistema de notas simples, permitindo que usuários criem, acessem e gerem resumos de notas de texto via URIs e ferramentas.

Electron MCP Server

A Model Context Protocol server that provides comprehensive Electron application automation, debugging, and observability capabilities through Chrome DevTools Protocol integration.

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.

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.

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!

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.

svelte-llm

Svelte and SvelteKit developer odcumentation

Real-Time Bidding MCP Server

An MCP (Multi-Agent Conversation Protocol) Server that provides access to Google's Real-Time Bidding API, enabling programmatic interactions with RTB functionalities through natural language.