Discover Awesome MCP Servers

mcp-server-kubernetes

Espejo de

ezmcp

Easy-to-use MCP server framework specialized for SSE.

MCP TTS Say

MCP Server Tool for Text To Speech

Letta MCP Server

Espejo de

testmcpgithubdemo

created from MCP server demo

Awesome-Medical-MCP-Servers

Una colección de servidores de Medical MCP.

CLI MCP Server

Mirror of

graphexpert2025

Okay, here are comprehensive notes on Graph Databases and MCP (presumably Media Content Platform) Servers, designed to provide enough context for a Large Language Model (LLM) to quickly understand and contribute to Graph Database projects in that domain. **I. Graph Databases: Core Concepts** * **What are they?** Graph databases are NoSQL databases that use graph structures with nodes, edges, and properties to store and represent data. They excel at managing and querying relationships between data points. * **Key Components:** * **Nodes (Vertices):** Represent entities (e.g., users, movies, actors, devices, content items). * **Edges (Relationships):** Represent connections between nodes (e.g., "ACTED_IN," "FOLLOWS," "RECOMMENDS," "IS_RELATED_TO"). Edges have a direction (source and target node). * **Properties:** Key-value pairs that store attributes of nodes and edges (e.g., node: `user.name = "Alice"`, edge: `ACTED_IN.role = "Lead"`). * **Why use them?** * **Relationship-Centric Data:** Ideal for data where relationships are as important as the data itself. Think social networks, recommendation engines, knowledge graphs, fraud detection, and content management. * **Performance on Complex Relationships:** Graph databases traverse relationships efficiently, often outperforming relational databases for complex queries involving multiple joins. * **Flexibility:** Easier to evolve the data model than relational databases. You can add new node types, edge types, and properties without major schema changes. * **Intuitive Modeling:** The graph model often mirrors the real-world domain more closely, making it easier to understand and design. * **Common Use Cases (Relevant to MCP):** * **Content Recommendation:** Suggesting movies, TV shows, or articles based on user viewing history, preferences, and relationships between content items (e.g., similar genres, actors, directors). * **Content Discovery:** Helping users find related content they might not have known about. * **Rights Management:** Modeling content rights and restrictions based on geography, device, and user roles. Determining if a user is allowed to access specific content. * **Metadata Management:** Storing and querying rich metadata about content, including actors, directors, genres, keywords, and production details. * **Lineage Tracking:** Tracing the origin and transformations of content assets throughout the MCP workflow. * **Personalized Experiences:** Tailoring content presentation and features based on user profiles and relationships. * **Graph Query Languages:** * **Cypher (Neo4j):** A declarative graph query language that is relatively easy to learn and use. It uses an ASCII art-like syntax to describe graph patterns. Example: ```cypher MATCH (user:User {userId: "123"})-[:WATCHED]->(movie:Movie) RETURN movie.title ``` * **Gremlin (Apache TinkerPop):** A graph traversal language that is more programmatic and flexible. It's often used with graph databases that support the TinkerPop standard. * **GraphQL:** While not strictly a graph database query language, GraphQL can be used to query data from a graph database via an API layer. * **Popular Graph Database Systems:** * **Neo4j:** The most popular graph database. It's a native graph database with strong ACID properties. * **Amazon Neptune:** A fully managed graph database service on AWS. * **Azure Cosmos DB (with Gremlin API):** A globally distributed, multi-model database service that supports graph data. * **JanusGraph:** An open-source, distributed graph database. * **Key Considerations:** * **Data Modeling:** Designing the graph schema (nodes, edges, properties) is crucial for performance and queryability. Think carefully about the relationships you need to model. * **Scalability:** Consider the size of your graph and the expected query load. Choose a graph database that can scale to meet your needs. * **Transactions:** Ensure that your graph database supports ACID transactions to maintain data consistency. * **Indexing:** Use indexes to improve query performance, especially on frequently queried properties. * **Graph Algorithms:** Explore graph algorithms (e.g., PageRank, shortest path, community detection) to gain insights from your data. **II. MCP (Media Content Platform) Servers: Context** * **What is an MCP Server?** An MCP server is a system that manages and delivers media content. It's a broad term, but in this context, it likely refers to a platform that handles: * **Ingestion:** Receiving media content from various sources (e.g., uploads, transcoding pipelines). * **Storage:** Storing media assets (video, audio, images, metadata). * **Metadata Management:** Storing and managing metadata about content (e.g., title, description, actors, genres, rights information). * **Transcoding:** Converting media content into different formats and resolutions for various devices. * **Delivery:** Streaming or downloading media content to users. * **Rights Management (DRM):** Protecting content from unauthorized access and distribution. * **Search and Discovery:** Allowing users to find content. * **Personalization:** Tailoring content recommendations and experiences to individual users. * **Analytics:** Tracking content usage and user behavior. * **Key Components of an MCP Server (Conceptual):** * **Ingest API:** For receiving content and metadata. * **Metadata Store:** Where metadata is stored (this is where the Graph Database comes in). * **Content Store:** Where the actual media files are stored (e.g., object storage like AWS S3 or Azure Blob Storage). * **Transcoding Engine:** For converting media formats. * **Delivery API:** For streaming or downloading content. * **Rights Management System (DRM):** For protecting content. * **Search Index:** For indexing content metadata for search. * **Recommendation Engine:** For suggesting content to users. * **Analytics Engine:** For collecting and analyzing usage data. * **How Graph Databases Fit into MCP Servers:** * **Metadata Management:** Graph databases are excellent for storing and querying complex metadata about content. They can model relationships between movies, actors, directors, genres, keywords, and other entities. * **Recommendation Engines:** Graph databases can be used to build recommendation engines that suggest content based on user viewing history, preferences, and relationships between content items. * **Rights Management:** Graph databases can model content rights and restrictions based on geography, device, and user roles. * **Content Discovery:** Graph databases can help users find related content they might not have known about. * **Lineage Tracking:** Graph databases can track the origin and transformations of content assets throughout the MCP workflow. * **Example Graph Schema for an MCP Server:** ``` Nodes: User: {userId, name, age, location, preferences} Movie: {movieId, title, description, releaseDate, duration} TVShow: {tvShowId, title, description, releaseDate} Episode: {episodeId, title, description, episodeNumber, seasonNumber} Actor: {actorId, name} Director: {directorId, name} Genre: {genreId, name} Keyword: {keywordId, name} Device: {deviceId, type, location} Edges: WATCHED: (User)->(Movie) {timestamp} WATCHED: (User)->(Episode) {timestamp} ACTED_IN: (Actor)->(Movie) {role} ACTED_IN: (Actor)->(Episode) {role} DIRECTED: (Director)->(Movie) DIRECTED: (Director)->(Episode) HAS_GENRE: (Movie)->(Genre) HAS_GENRE: (Episode)->(Genre) HAS_KEYWORD: (Movie)->(Keyword) HAS_KEYWORD: (Episode)->(Keyword) IS_PART_OF: (Episode)->(TVShow) RECOMMENDS: (User)->(Movie) RECOMMENDS: (User)->(Episode) ALLOWED_TO_VIEW: (User)->(Movie) {geography, deviceType} // For rights management ALLOWED_TO_VIEW: (User)->(Episode) {geography, deviceType} // For rights management RELATED_TO: (Movie)->(Movie) {similarityScore} RELATED_TO: (Episode)->(Episode) {similarityScore} ``` * **Example Queries (Cypher):** * **Find movies watched by a user:** ```cypher MATCH (user:User {userId: "123"})-[:WATCHED]->(movie:Movie) RETURN movie.title ``` * **Find actors who acted in a specific movie:** ```cypher MATCH (movie:Movie {movieId: "456"})<-[:ACTED_IN]-(actor:Actor) RETURN actor.name ``` * **Find movies similar to a movie the user watched:** ```cypher MATCH (user:User {userId: "123"})-[:WATCHED]->(movie:Movie)-[:RELATED_TO]->(similarMovie:Movie) RETURN similarMovie.title, similarMovie.description ``` * **Find if a user is allowed to view a movie on a specific device in a specific location:** ```cypher MATCH (user:User {userId: "123"})-[:ALLOWED_TO_VIEW {geography: "USA", deviceType: "Mobile"}]->(movie:Movie {movieId: "456"}) RETURN movie ``` **III. Considerations for LLMs** * **Data Access:** The LLM needs access to the graph database (or an API that exposes graph data) to answer questions and perform tasks. * **Query Generation:** The LLM can be used to generate graph queries (e.g., Cypher or Gremlin) from natural language. This requires training the LLM on graph query syntax and the specific graph schema. * **Knowledge Graph Completion:** The LLM can be used to enrich the graph database by extracting new entities and relationships from text data. * **Reasoning:** The LLM can reason over the graph data to answer complex questions and make inferences. * **Data Security and Privacy:** Ensure that the LLM is not exposed to sensitive data and that it adheres to privacy regulations. * **Prompt Engineering:** Crafting effective prompts is crucial for getting the LLM to perform the desired tasks. Provide the LLM with context about the graph schema and the types of questions it should answer. **IV. Key Terms and Acronyms** * **MCP:** Media Content Platform * **DRM:** Digital Rights Management * **ACID:** Atomicity, Consistency, Isolation, Durability (database transaction properties) * **NoSQL:** Not Only SQL (a type of database that doesn't use the traditional relational model) * **API:** Application Programming Interface * **LLM:** Large Language Model **V. Next Steps for an LLM** 1. **Understand the Specific Graph Schema:** Get a detailed description of the nodes, edges, and properties used in the MCP's graph database. 2. **Learn the Query Language:** Become proficient in the graph query language used (e.g., Cypher or Gremlin). 3. **Explore Example Queries:** Study example queries to understand how to retrieve data from the graph. 4. **Understand the MCP Architecture:** Learn how the graph database integrates with the other components of the MCP server. 5. **Identify Use Cases:** Focus on specific use cases where the LLM can add value (e.g., query generation, knowledge graph completion, reasoning). This comprehensive overview should provide a solid foundation for an LLM to quickly get up to speed on Graph Database projects within the context of MCP servers. Remember to tailor the information to the specific implementation and requirements of the project. Good luck!

mcp-server-collector MCP server

Mirror of

Laravel Artisan MCP Server

Un servidor de Protocolo de Contexto de Modelo (MCP) que permite la ejecución segura de comandos de Laravel Artisan a través de Claude y otros clientes MCP.

Setup

An MCP Server for Ableton Live

Model Context Protocol Server

Here's a translation of "A Model Context Protocol server implementation with FastAPI" into Spanish: **Opción 1 (Más literal):** * Una implementación de servidor del Protocolo de Contexto del Modelo con FastAPI **Opción 2 (Más fluida y común):** * Implementación de un servidor del Protocolo de Contexto del Modelo usando FastAPI **Opción 3 (Enfatizando la creación):** * Implementación de un servidor para el Protocolo de Contexto del Modelo con FastAPI **Explanation of Choices:** * **"A" vs. "Una/Un":** The indefinite article "A" can be translated as "Una" (feminine) or "Un" (masculine). Since "implementación" (implementation) is feminine, "Una" is the correct choice for the more literal translation. However, in Spanish, it's often more natural to omit the indefinite article, especially when the noun is the subject of the sentence. * **"with" vs. "con/usando":** "With" can be translated as "con" (with) or "usando" (using). "Usando" is often preferred when referring to using a tool or framework. * **"Model Context Protocol" vs. "Protocolo de Contexto del Modelo":** The order of the words is reversed to follow typical Spanish noun-adjective order. **Recommendation:** I recommend **Opción 2: Implementación de un servidor del Protocolo de Contexto del Modelo usando FastAPI** because it sounds the most natural and idiomatic in Spanish.

Selector_MCP_Server

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A modern desktop client for Claude AI with MCP server support, built with Tauri, React, and TypeScript.

local-command-server MCP Server

Mirror of

Naver Maps MCP Server

Model Context Protocol (MCP) 서버를 이용한 네이버 지도 API 구현

IBKR MCP Server

Servidor MCP para el Cliente de IBKR

mcp-server-curio

El servidor MCP se utiliza para el proyecto Curio del ecosistema Filecoin.

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Next.js ile oluşturulmuş MCP Server projesi

sek-fx-mcp

Un servidor de Protocolo de Contexto de Modelo (MCP) que conecta un LLM a la API del Riksbank para el tipo de cambio de la corona sueca.

MCP Server

A middleware server that acts as a bridge between Cursor IDE and AI models, validating AI responses using project context and Gemini.

Air-Pollution

Este proyecto es un Servidor de Protocolo de Contexto de Modelo (MCP) construido con Node.js + Express.js que interactúa con la API de OpenWeather. Permite a los usuarios obtener datos de contaminación del aire basados en latitud y longitud o ciudad y país. El backend garantiza una gestión segura de la clave API utilizando dotenv y gestiona las solicitudes a la API de manera eficiente.

Go Delve Debugger MCP Server

MCP Server that exposes Golang's Delve Debugger, enable AI to self-debug

Financial Analysis MCP Server

Un servidor de Protocolo de Contexto de Modelo (MCP) de Anthropic para análisis financiero con integración de las API de alphavantage.com y financialmodellingprep.com.

MCP File Finder

MCP server on Python

Claude MCP (Master Control Program)

MCP server with different tools to extend Claude-code functionalities.

say-mcp-server

Mirror of