Discover Awesome MCP Servers

MCP Data Analytics Server

A FastMCP-based server that provides data analysis tools for processing, analyzing, and visualizing data with an intuitive Streamlit web interface.

FastMCP_RecSys

A CLIP-Based Fashion Recommender system that allows users to upload clothing images and receive tags and recommendations based on visual analysis.

Shortcut MCP Server

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

pyATS MCP Server

Un servidor MCP para pyATS (experimental)

Upstage MCP Server

Un servidor de Protocolo de Contexto de Modelo para analizar documentos utilizando la API de digitalización de documentos de Upstage AI.

Jira Assets MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Jira Assets (beta): https://www.cdata.com/download/download.aspx?sku=KNZK-V&type=beta

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.

TickTick MCP Server

An MCP server that enhances TickTick workflow by providing comprehensive task management tools with improved filtering capabilities, allowing AI assistants and MCP-compatible applications to interact with TickTick tasks with greater precision.

KPC Component Library MCP Server

AI code generation service for KPC component library that solves hallucination problems by providing accurate component APIs, validation rules, and usage examples.

mcp-servers

Repository for Model Context Protocol Servers

Investment Memorandum Processor MCP Server

An MCP server that automates extraction of structured financial data from investment memorandums and generates standardized PowerPoint presentations through a RESTful API.

OpenAPITools SDK

Tus APIs, ahora herramientas de IA. Construye servidores de MCP en un minuto.

Obsidian MCP Tool Server

Provides an MCP server that allows AI assistants to interact with Obsidian vaults, enabling reading/writing notes, managing metadata, searching content, and working with daily notes.

Daisys MCP Server

A beta server that enables integration with Daisys.ai services via the Message Control Protocol (MCP), allowing AI clients like Claude Desktop and Cursor to use Daisys features through a standardized interface.

Prefect MCP Server

Espejo de

MCP DuckDuckGo Search Server

Permite la integración con las capacidades de búsqueda de DuckDuckGo para LLM, admitiendo búsqueda web integral, filtrado regional, tipos de resultados y navegación segura con almacenamiento en caché y parámetros de búsqueda personalizables.

Neon MCP Server

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

Malaysia Open Data MCP

MCP server providing access to Malaysia's government datasets, dashboards, weather forecasts, and transport data through a unified search system and hybrid data access architecture.

Airtable MCP Server by CData

Airtable MCP Server by CData

ProAlpha MCP Server

A Model-Context-Protocol server that provides a read-only interface to ProAlpha MSSQL databases, automatically capturing database schemas and enabling data analysis through SQL queries.

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!)

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.

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

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.

alpha-ticker-mcp

An MCP server that streams real-time Binance Alpha token prices and liquidity data, powering your AI agents and workflows with instant alpha market insights.