Discover Awesome MCP Servers

MCP Music Studio

Two-mode creative music studio for AI: scored composition (ABC notation with sheet music) and live performance (Strudel live coding with TidalCycles), with interactive UI rendering inline in Claude Desktop and other MCP clients.

mcp-devops-deploy

Enables automated deployments, environment management, snapshots, and deployment monitoring through DevOps Deploy's REST API.

@fastly/mcp

Enables assistants to interact with the Fastly API, allowing them to manage services, domains, TLS settings, traffic, dictionaries, ACLs, purge content, and make configuration changes.

UnityInfoMCP

A runtime inspection and automation toolkit that enables MCP clients to interact with live Unity game sessions through a dedicated bridge plugin. It allows users to browse scene hierarchies, inspect component fields, search text elements, and modify game object properties in real-time.

Ultra MCP-SS

A FastAPI-based MCP server that integrates with smartscreen.tv, allowing you to programmatically control web displays by displaying media, sending notifications, and controlling playback via HTTP commands.

Bring! MCP Server

MCP server for the Bring! shopping list API, enabling management of shopping lists via natural language.

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.

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!)

clawboard-mcp-server

Enables AI agents to interact with ClawBoard tasks, including listing, searching, viewing details, posting comments, and upvoting.

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.

kusto-mcp

Enables AI assistants to query Azure Data Explorer using natural language, eliminating the need to write KQL.

remote-mcp-server-authless

Deploy a remote MCP server on Cloudflare Workers without authentication, enabling use with Cloudflare AI Playground and local clients via mcp-remote proxy.

Awesome MCP Servers

Una lista seleccionada de servidores y herramientas del Protocolo de Contexto del Modelo (MCP). Descubre y explora varias implementaciones de MCP que permiten a los modelos de IA conectarse con fuentes de datos y herramientas externas.

🛡️ BurpSuite MCP Server

Mirror of

NotebookLM MCP Server

Enables AI agents to programmatically access Google NotebookLM through browser automation for managing notebooks, sources, and chat interactions. It supports automated content generation including audio overviews, study guides, and quizzes directly within AI workflows.

Alibaba Cloud MCP Server

Enables AI assistants to manage Alibaba Cloud resources via natural language, with explicit tools for common services and a universal API invoker for full cloud coverage.

Whoisfreaks

A Model Context Protocol (MCP) server that exposes the full WhoisFreaks API suite as AI-callable tools. Works with Claude Desktop, Cursor, Windsurf, VS Code, Continue, Zed, and any other MCP-compatible AI client.

Shortcut MCP Server

A Model Context Protocol server that integrates Shortcut project management functionality with AI assistants like Cursor, Windsurf, Claude Code, and Zed.

Neon MCP Server

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

HeroUI MCP Server

Provides AI assistants with access to HeroUI component library documentation, enabling them to list components, retrieve detailed API references, and explore customization options for building React applications.

facebook-ads-mcp-server

MCP server for Meta/Facebook Marketing API allowing you to view and manage ad accounts, campaigns, ad sets, ads, and creatives, as well as fetch insights and upload ad images.

Bootpay Developer Docs MCP Server

Enables AI coding tools to search and retrieve Bootpay payment and commerce developer documentation, including integration guides and customer service manuals. It facilitates tasks such as payment linking, billing key issuance, and webhook configuration through natural language queries.

transcode-mcp

A media transcoding proxy with an MCP control surface for generating optimized media URLs, primarily for offline delivery in Bible translation projects.

MCP ContentEngineering

Enables direct access to raw Markdown content from files or directories without processing. Perfect for providing AI models with business rules, documentation, or knowledge bases exactly as written.

mcp-destatis

Enables querying Germany's official statistics from Destatis GENESIS-Online through natural language or direct tool calls.

HexagonML ModelManager MCP Server

Provides a Model Context Protocol interface for HexagonML ModelManager tools, enabling interaction with machine learning model management functionality through natural language.

Strava MCP Server

Enables users to interact with their Strava data through natural language to analyze workouts, track fitness progress, and explore routes. It supports retrieving detailed activity stats, heart rate data, and segment insights directly within AI assistants.

codebase-context-mcp

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.