Discover Awesome MCP Servers

LiveKit RAG Assistant

Enables AI-powered semantic search and question-answering for LiveKit documentation using Pinecone vector search and real-time web search with Tavily, providing detailed responses with source attribution.

QI140 MCP Multi

A multi-purpose MCP server for AI-powered healthcare assistance that processes clinical data through configurable LLM providers. Maintains webhook endpoints for patient data processing with PostgreSQL logging and OpenAPI documentation.

Aristotle MCP Server

Enables LLMs to prove theorems in Lean and formalize mathematical problems using the Aristotle API, supporting both formal Lean code and natural language problem submissions.

Alternative Payments MCP Server

Provides AI assistants with read and safe-write access to Alternative Payments customers, invoices, payment requests, transactions, payouts, and webhooks without directly moving money.

MySQL MCP Server

Enables AI assistants to securely interact with MySQL databases for schema discovery, data querying, and record management with configurable access controls. It provides specialized tools for listing tables, describing structures, and performing CRUD operations within environments like Claude and VS Code.

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.

marsnme

Agent-agnostic persistent memory backend. 13 MCP tools, Supabase + Jina embeddings, multi-profile isolation, semantic recall across sessions.

Shell Command MCP Server

Permite ejecutar comandos de shell dentro de un contenedor Docker seguro a través de la interfaz MCP de Claude, proporcionando herramientas de Kubernetes y un entorno aislado sin acceso al demonio Docker del host.

vision-memory-mcp

Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.

HiveExchange

Enables autonomous agents to interact with prediction markets, perpetuals, and derivatives on HiveExchange with real USDC settlement on Base L2.

OpenWeatherMap MCP Server

A Model Context Protocol server that provides comprehensive weather data and forecasts through the OpenWeatherMap API, enabling AI assistants to access real-time weather information, forecasts, air quality data, and location services.

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.

jira-dc-advops-mcp

Local MCP helpers for Jira Data Center advanced ops used from Cursor. It enables reading Tempo Structure boards via Structure REST API.

erbina

Bootstraps dev environments by installing, wiring, and verifying CLI tools and MCP servers from curated recipes, and provides scope-aware audit of MCP configurations.

opencode-openai-vision-mcp

Provides image/vision support for OpenCode by routing images to any OpenAI-compatible endpoint, enabling paste-and-ask screenshot workflows.

Unanet GovCon ERP MCP Server

Enables natural language interaction with Unanet GovCon ERP for project management, time tracking, financial reporting, and contact management, turning complex navigation into simple questions.

me-db

Enables Claude to read, write, and search markdown notes stored in a private git repo via an MCP server integrated with Silverbullet editor.

Testing Agent MCP Server

Provides self-healing UI/API test automation via Plan-Generate-Run-Heal cycle, exposing MCP tools for test platforms to trigger batches and retrieve reports.

MCP UJI Academic Server

Enables querying academic data such as subjects, degrees, locations, and schedules from Universitat Jaume I via MCP tools.

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!)

Neon MCP Server

Te permite usar Claude Desktop, o cualquier cliente MCP, para usar el lenguaje natural para lograr cosas con Neon.

dataverse-mcp-server

MCP server for Microsoft Dataverse API with safe-by-default configuration. Works with any Dataverse / Dynamics 365 environment.

Feather MCP Server

Enables AI agents to automate Facebook lead generation and CRM tasks such as searching leads, sending friend requests, managing contacts, broadcasting messages, and tracking growth metrics.

grok-mcp

Exposes local Grok Build bridge as MCP stdio server, enabling code agents like Claude Code to run Grok models, check status, and manage runs.

Superpowers MCP Server

Provides access to the Superpowers skills library - expert-crafted workflows and best practices that guide AI assistants through proven techniques for coding tasks. Supports both community skills and custom personal skills.

Spotify MCP

A FastMCP server that enables searching tracks, controlling playback (play, pause, skip), and retrieving currently playing content on Spotify via PKCE authentication.

dsh-vision

Provides image understanding capabilities via local OCR and cloud VLM, enabling text extraction and visual description through MCP.

data-olympus MCP server

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

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