Discover Awesome MCP Servers

Android Project MCP Server

Un servidor de Protocolo de Contexto de Modelo que permite construir proyectos de Android y ejecutar pruebas directamente en Visual Studio Code a través de extensiones como Cline o Roo Code.

Gremlin Web Scraper MCP

A lightweight HTTP module that enables scraping visible text from any publicly accessible webpage, integrating directly with VS Code's MCP system.

Rag chatbot with a localhost MCP server

Okay, I understand. You're building a Retrieval-Augmented Generation (RAG) based HR chatbot that will provide information about workplace rules, and you're planning to use an MCP (presumably a Message Communication Platform or similar) server. Here's a breakdown of the key components and considerations for this project, along with Spanish translations for common terms: **1. Core Components (Componentes Principales):** * **HR Knowledge Base (Base de Conocimiento de RR.HH.):** This is your collection of workplace rules, policies, and procedures. It needs to be well-organized and easily searchable. * *Spanish:* *Base de Conocimiento de Recursos Humanos* * **Embedding Model (Modelo de Incrustación):** This converts your text data (rules, policies) into numerical representations (embeddings) that capture the semantic meaning. Common choices include Sentence Transformers, OpenAI Embeddings, or Cohere Embeddings. * *Spanish:* *Modelo de Incrustación* or *Modelo de Vectores Semánticos* * **Vector Database (Base de Datos Vectorial):** This stores the embeddings, allowing for efficient similarity searches. Examples include Pinecone, Chroma, Weaviate, or FAISS. * *Spanish:* *Base de Datos Vectorial* * **Retrieval Component (Componente de Recuperación):** This takes the user's question, converts it into an embedding, and searches the vector database for the most relevant documents. * *Spanish:* *Componente de Recuperación* * **Large Language Model (LLM) (Modelo de Lenguaje Grande):** This takes the retrieved documents and the user's question and generates a coherent and informative answer. Examples include GPT-3.5, GPT-4, Llama 2, or other open-source models. * *Spanish:* *Modelo de Lenguaje Grande (LLM)* * **MCP Server Integration (Integración con el Servidor MCP):** This allows the chatbot to interact with users through your chosen messaging platform. You'll need to use the MCP's API to send and receive messages. * *Spanish:* *Integración con el Servidor MCP* **2. Workflow (Flujo de Trabajo):** 1. **User Asks a Question (El Usuario Hace una Pregunta):** The user sends a question to the chatbot via the MCP. * *Spanish:* *El usuario hace una pregunta* 2. **Question Embedding (Incrustación de la Pregunta):** The user's question is converted into an embedding using the same embedding model used for the knowledge base. * *Spanish:* *Incrustación de la pregunta* 3. **Similarity Search (Búsqueda de Similitud):** The embedding is used to search the vector database for the most relevant documents. * *Spanish:* *Búsqueda de Similitud* 4. **Contextualization (Contextualización):** The retrieved documents are combined with the user's question to provide context for the LLM. * *Spanish:* *Contextualización* 5. **Answer Generation (Generación de la Respuesta):** The LLM generates an answer based on the context. * *Spanish:* *Generación de la Respuesta* 6. **Response to User (Respuesta al Usuario):** The chatbot sends the answer back to the user via the MCP. * *Spanish:* *Respuesta al Usuario* **3. Key Considerations (Consideraciones Clave):** * **Data Quality (Calidad de los Datos):** The accuracy and completeness of your HR knowledge base are crucial. Ensure the information is up-to-date and well-written. * *Spanish:* *Calidad de los Datos* * **Embedding Model Choice (Elección del Modelo de Incrustación):** Choose an embedding model that is appropriate for your data and language. Consider models specifically trained for semantic similarity. * *Spanish:* *Elección del Modelo de Incrustación* * **LLM Choice (Elección del LLM):** Consider the cost, performance, and capabilities of different LLMs. Fine-tuning the LLM on your HR data can improve accuracy and relevance. * *Spanish:* *Elección del LLM* * **MCP Integration (Integración con MCP):** Understand the API of your MCP server and how to send and receive messages programmatically. * *Spanish:* *Integración con MCP* * **Security (Seguridad):** Implement appropriate security measures to protect sensitive HR data. * *Spanish:* *Seguridad* * **Scalability (Escalabilidad):** Design your system to handle a large number of users and requests. * *Spanish:* *Escalabilidad* * **User Experience (Experiencia del Usuario):** Make the chatbot easy to use and understand. Provide clear instructions and helpful feedback. * *Spanish:* *Experiencia del Usuario* * **Prompt Engineering (Ingeniería de Prompts):** Craft effective prompts for the LLM to ensure it generates accurate and relevant answers. This includes providing clear instructions and examples. * *Spanish:* *Ingeniería de Prompts* * **Evaluation (Evaluación):** Regularly evaluate the performance of the chatbot and make adjustments as needed. Track metrics such as accuracy, relevance, and user satisfaction. * *Spanish:* *Evaluación* **4. Example Technologies/Libraries (Ejemplos de Tecnologías/Librerías):** * **Python:** A popular language for building chatbots and working with LLMs. * *Spanish:* *Python* * **Langchain:** A framework for building applications powered by LLMs. * *Spanish:* *Langchain* (usually used as is) * **LlamaIndex (GPT Index):** Another framework for building applications powered by LLMs, focused on data indexing and retrieval. * *Spanish:* *LlamaIndex* (usually used as is) * **FastAPI/Flask:** Frameworks for building APIs to connect the chatbot to the MCP server. * *Spanish:* *FastAPI/Flask* (usually used as is) **5. Example Code Snippet (Conceptual - Python with Langchain):** ```python # Conceptual example - requires setup of vectorstore, LLM, and MCP integration from langchain.chains import RetrievalQA from langchain.document_loaders import TextLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.llms import OpenAI from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Chroma # 1. Load documents (Cargar documentos) loader = TextLoader("workplace_rules.txt") documents = loader.load() # 2. Split documents (Dividir documentos) text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) # 3. Create embeddings (Crear incrustaciones) embeddings = OpenAIEmbeddings() # 4. Create vectorstore (Crear base de datos vectorial) db = Chroma.from_documents(texts, embeddings) # 5. Create retriever (Crear recuperador) retriever = db.as_retriever() # 6. Create LLM (Crear LLM) llm = OpenAI() # 7. Create QA chain (Crear cadena de preguntas y respuestas) qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever) # 8. Get user question from MCP (Obtener pregunta del usuario desde MCP) user_question = get_user_question_from_mcp() # Replace with your MCP integration # 9. Generate answer (Generar respuesta) answer = qa.run(user_question) # 10. Send answer to user via MCP (Enviar respuesta al usuario a través de MCP) send_answer_to_user_via_mcp(answer) # Replace with your MCP integration print(answer) ``` **Important Notes:** * **MCP Specifics:** The most crucial part is the integration with your MCP server. You'll need to consult the MCP's documentation to understand how to send and receive messages. This will likely involve using their API. * **Error Handling:** Implement robust error handling to gracefully handle unexpected situations. * **Testing:** Thoroughly test your chatbot to ensure it provides accurate and helpful information. * **Iterative Development:** Start with a small set of rules and policies and gradually expand your knowledge base. Continuously evaluate and improve the chatbot's performance. This detailed breakdown should give you a solid foundation for building your RAG-based HR chatbot. Remember to adapt the specific technologies and techniques to your specific needs and resources. Good luck! (¡Buena suerte!)

ai-test-gen-poc

An AI-powered test script generator that uses Playwright MCP Server to generate test scripts in TypeScript using BDD-style prompts

Morphik MCP

Enables interaction with the Morphik multi-modal database system for document ingestion, retrieval, querying, and management. Supports text and file ingestion, semantic search with LLM-powered completions, and file system navigation with security controls.

Canvas MCP Server

Connects Canvas LMS to AI assistants, enabling users to list courses and retrieve assignment details through natural language. It features secure multi-institution support with encrypted token storage and a simplified setup process for students.

Jumpseller API MCP Server

An MCP Server that provides access to the Jumpseller e-commerce platform API, allowing users to interact with Jumpseller's functionality through natural language commands.

Binance Futures MCP Server

Provides comprehensive access to Binance Futures API endpoints for account management, real-time market data retrieval, and order execution. It supports 17 essential trading tools including position tracking, smart ticker caching, and risk management functionality.

Festival Finder

Provides access to performance and festival information across South Korea using the KOPIS (Korea Performance Information System) API. Supports multiple platform integrations including KakaoTalk and web APIs through a modular adapter architecture.

modbus-mcp

modbus-mcp

knownissue

Shared debugging memory for AI coding agents. Agents search, report, patch, and verify bug fixes through 5 MCP tools. Verified by proof, not upvotes.

SportIntel MCP Server

Provides AI-powered sports analytics for Daily Fantasy Sports (DFS) with real-time player projections, lineup optimization, live odds aggregation from multiple sportsbooks, and SHAP-based explainability to understand recommendation reasoning.

GitHub PR Review Server

Enables automated GitHub Pull Request reviews using local Ollama, Cursor CLI, or Gemini CLI as AI providers. Supports customizable review prompts, comprehensive PR analysis, and optional auto-posting of reviews to GitHub.

Prismic Content MCP

Enables interaction with Prismic through tools for reading documents, managing media assets, and performing content migrations or updates. Supports the Prismic Content, Asset, Migration, and Custom Types APIs with secure write operations and multi-transport support.

MCPControl

A Windows control server built using nut.js and Model Context Protocol (MCP), providing programmatic control over Windows system operations including mouse, keyboard, window management, and screen capture functionality.

Trip Planner MCP

Enables multi-agent trip planning with coordinated specialized agents for team management, food preferences, and travel recommendations. Integrates Neo4j employee graphs, Postgres data, real-time weather APIs, and country information for comprehensive travel planning.

Biel AI

Let AI tools like Cursor, VS Code, or Claude Desktop answer questions using your product docs. Biel.ai provides the RAG system and MCP server.

MCP MQTT Server

Enables LLM agents to interact with MQTT brokers through publish, subscribe, and query operations. Provides fine-grained topic permissions with wildcard support for secure IoT device communication and sensor data access.

MCP Calendar

Okay, here's the translation of "MCP Google Calendar Server with python" into Spanish, along with some context and considerations: **Translation Options:** * **Direct Translation (Most Literal):** "Servidor de Calendario de Google MCP con Python" * **Slightly More Natural (If "MCP" is an acronym you want to keep):** "Servidor de Calendario de Google MCP, implementado con Python" (This adds "implemented with" for clarity) * **If "MCP" needs explanation (and you know what it stands for):** "Servidor de Calendario de Google [Acrónimo MCP], implementado con Python" (Replace "[Acrónimo MCP]" with the actual expanded acronym in Spanish, if it exists. For example, if MCP stood for "Multi-Client Protocol", you might use "Servidor de Calendario de Google Protocolo Multi-Cliente, implementado con Python") **Explanation and Considerations:** * **"Servidor de Calendario de Google":** This is the standard translation of "Google Calendar Server." * **"con Python" / "implementado con Python":** "con Python" is a direct translation of "with Python." "implementado con Python" (implemented with Python) is often preferred in technical contexts because it clarifies that Python is the language used to build or interact with the server. * **"MCP":** The biggest question is what "MCP" stands for. If it's a well-known acronym, you might leave it as is. If it's specific to a project or company, you might need to provide the full name in Spanish (if there's a standard translation) or explain it. If it's a code name, you might leave it as "MCP" and add a brief explanation in parentheses if necessary. **Recommendation:** Unless you know what "MCP" stands for and if it has a standard Spanish translation, I would recommend: **"Servidor de Calendario de Google MCP, implementado con Python"** This is clear, relatively natural, and preserves the "MCP" identifier. If you can provide the meaning of "MCP," I can give you a more precise translation.

Stream

Stream MCP server https://streampay.sa/

LangChain Agent MCP Server

Exposes LangChain agent capabilities through the Model Context Protocol, enabling multi-step reasoning tasks with ReAct pattern execution via a production-ready FastAPI service deployed on Google Cloud Run.

Dialogflow MCP Server

An MCP server that enables natural language interactions with Google's Dialogflow API, allowing users to create and manage conversational agents, intents, and entities through the Dialogflow v3beta1 API.

Image Description MCP Server

Enables AI assistants to analyze images from URLs or local files using xAI's Grok API. Provides detailed image descriptions, technical metadata extraction, and optical character recognition (OCR) capabilities.

Malaysia Prayer Time MCP Server

Espejo de

Mem0 MCP Server

Enables persistent memory storage and retrieval for AI conversations using Mem0, with semantic search capabilities backed by local Postgres and Qdrant vector database.

NGSS MCP Server

Provides programmatic access to Next Generation Science Standards (NGSS) for middle school education with 8 powerful tools for standard lookup, search, filtering by 3D framework components, and intelligent curriculum unit planning.

amCharts 5 MCP Server

MCP server that gives AI assistants on-demand access to 1,500+ amCharts 5 docs, ~300 code examples, and 1000+ class API references.

Learning Hour MCP

An MCP server that generates comprehensive Learning Hour content for Technical Coaches, enabling teams to practice technical excellence through structured deliberate practice sessions.

Root Signals MCP Server

Root Signals MCP Server