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CLI MCP Server

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Setup

An MCP Server for Ableton Live

Luke Desktop

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

graphexpert2025

Okay, here are comprehensive notes on Graph Databases and MCP (Media Content Platform) Servers, designed to provide enough context for a Large Language Model (LLM) to quickly understand and contribute to Graph Database projects within an MCP environment. **I. Graph Databases: Core Concepts** * **What are Graph Databases?** * A type of NoSQL database that uses graph structures with nodes, edges, and properties to store and represent data. Focus is on relationships between data points. * **Nodes (Vertices):** Represent entities (e.g., users, movies, products, documents). * **Edges (Relationships):** Represent connections between nodes (e.g., "watched," "recommended," "related_to"). Edges have direction (source and target node) and a type. * **Properties:** Key-value pairs that store attributes of nodes and edges (e.g., user's age, movie's release year, relationship's strength). * **Key Advantages:** * **Relationship-Centric:** Excellent for modeling and querying complex relationships. Outperforms relational databases when dealing with highly connected data. * **Performance:** Traversing relationships is highly optimized. Queries focus on finding patterns and connections, not full table scans. * **Flexibility:** Schema-less or schema-light. Easier to evolve the data model as requirements change. * **Intuitive Modeling:** The graph model closely mirrors real-world relationships, making it easier to understand and design. * **Common Use Cases:** * **Recommendation Engines:** Suggesting products, content, or connections based on user behavior and relationships. * **Social Networks:** Modeling user connections, groups, and interactions. * **Knowledge Graphs:** Organizing and querying factual information and relationships between entities. * **Fraud Detection:** Identifying patterns of fraudulent activity by analyzing relationships between accounts, transactions, and devices. * **Identity and Access Management (IAM):** Managing user permissions and access rights based on roles and relationships. * **Master Data Management (MDM):** Creating a unified view of data across different systems by linking related entities. * **Popular Graph Database Technologies:** * **Neo4j:** The leading graph database. Supports Cypher query language. Mature, well-documented, and has a large community. * **Amazon Neptune:** Managed graph database service on AWS. Supports both Gremlin and SPARQL query languages. * **JanusGraph:** Open-source, distributed graph database. Supports Gremlin. Designed for scalability and fault tolerance. * **Microsoft Azure Cosmos DB (with Gremlin API):** Globally distributed, multi-model database service. Supports Gremlin. * **Dgraph:** A distributed, scalable, and highly available graph database. Uses GraphQL-like query language. * **Query Languages:** * **Cypher (Neo4j):** A declarative graph query language designed to be easy to read and write. Uses a pattern-matching syntax. Example: `MATCH (user:User {name: 'Alice'})-[:FRIENDS_WITH]->(friend:User) RETURN friend` * **Gremlin (Apache TinkerPop):** A graph traversal language. More programmatic and flexible than Cypher. Example: `g.V().has('name', 'Alice').out('FRIENDS_WITH').values('name')` * **SPARQL:** A query language for RDF (Resource Description Framework) data. Often used with knowledge graphs. * **Key Considerations for Graph Database Projects:** * **Data Modeling:** Designing the graph schema (nodes, edges, properties) is crucial. Think about the relationships you want to model and the queries you need to support. * **Scalability:** Consider the size of your data and the expected query load. Choose a graph database that can scale to meet your needs. * **Query Performance:** Optimize your queries to avoid full graph traversals. Use indexes and appropriate data structures. * **Data Integration:** How will you load data into the graph database? Consider using ETL (Extract, Transform, Load) tools or custom scripts. * **Security:** Implement appropriate security measures to protect your graph data. **II. MCP (Media Content Platform) Servers: Context** * **What is an MCP Server?** * A system designed to manage, store, process, and deliver media content (videos, images, audio) at scale. It's a broad term, and the specific functionality can vary. * **Key Functions:** * **Ingestion:** Receiving media content from various sources (e.g., uploads, feeds, APIs). * **Storage:** Storing media files and metadata. Often uses cloud storage (e.g., AWS S3, Azure Blob Storage). * **Processing:** Transcoding media into different formats, generating thumbnails, extracting metadata, and performing other transformations. * **Metadata Management:** Storing and managing metadata about media content (e.g., title, description, tags, actors, categories). * **Delivery:** Serving media content to users via streaming protocols (e.g., HLS, DASH) or direct downloads. * **Search & Discovery:** Allowing users to search and find media content based on metadata and other criteria. * **Rights Management:** Enforcing copyright and licensing restrictions. * **Analytics:** Tracking usage and performance metrics. * **Typical Architecture:** * **Load Balancers:** Distribute traffic across multiple servers. * **API Gateway:** Provides a single entry point for accessing MCP services. * **Content Delivery Network (CDN):** Caches media content closer to users for faster delivery. * **Databases:** Store metadata, user information, and other data. This is where Graph Databases come in. * **Microservices:** The MCP is often built as a collection of microservices, each responsible for a specific function (e.g., transcoding, metadata extraction, search). * **Message Queue:** Used for asynchronous communication between microservices (e.g., RabbitMQ, Kafka). * **How Graph Databases Fit into MCPs:** * **Content Relationships:** Modeling relationships between media assets (e.g., "sequel_to," "remake_of," "related_to"). * **User Preferences:** Modeling user viewing history, ratings, and preferences to provide personalized recommendations. * **Metadata Enrichment:** Linking media assets to external knowledge graphs (e.g., Wikidata, IMDb) to enrich metadata. * **Rights Management:** Modeling licensing agreements and usage rights. * **Search & Discovery:** Enabling more sophisticated search queries based on relationships between media assets and other entities. * **Example:** Imagine a movie platform. A graph database could connect movies to actors, directors, genres, studios, and user viewing history. This allows for powerful recommendations like "Movies with the same director as the last movie you watched" or "Movies that are similar to movies liked by users with similar tastes." * **Key Considerations for Using Graph Databases in MCPs:** * **Data Volume:** MCPs often deal with large volumes of media content and metadata. Choose a graph database that can handle the scale. * **Query Performance:** Ensure that queries for recommendations and search are fast and efficient. * **Real-time Updates:** The graph database needs to be updated in real-time as new content is added and user behavior changes. * **Integration with Existing Systems:** The graph database needs to integrate with the other components of the MCP (e.g., content management system, CDN). **III. LM (Language Model) Considerations** * **How an LM can help with Graph Database projects in an MCP context:** * **Query Generation:** Given a natural language description of a query, the LM can generate the corresponding Cypher or Gremlin query. * **Data Modeling:** The LM can analyze existing data and suggest a graph schema. * **Metadata Extraction:** The LM can extract metadata from media content and populate the graph database. * **Relationship Discovery:** The LM can identify potential relationships between media assets based on their metadata. * **Code Generation:** The LM can generate code for loading data into the graph database, querying the graph database, and integrating the graph database with other systems. * **Documentation:** The LM can generate documentation for the graph database schema, queries, and APIs. * **Challenges for LMs:** * **Understanding Graph Semantics:** The LM needs to understand the meaning of nodes, edges, and properties in the graph. * **Handling Complex Queries:** Generating complex graph queries can be challenging. * **Data Bias:** The LM may be biased by the data it was trained on. * **Security:** The LM needs to be protected from malicious input that could compromise the graph database. * **Prompt Engineering for LMs:** * **Provide Context:** Give the LM as much context as possible about the graph database schema, the MCP architecture, and the specific task you want it to perform. * **Use Examples:** Provide examples of the desired output. * **Specify the Query Language:** Tell the LM which query language to use (e.g., Cypher, Gremlin). * **Use a Chain-of-Thought Approach:** Encourage the LM to break down the problem into smaller steps. * **Iterate and Refine:** Experiment with different prompts and refine them based on the LM's output. **IV. Example Scenario: Movie Recommendation Engine** Let's say we're building a movie recommendation engine for an MCP. Here's how a graph database could be used: * **Nodes:** * `Movie`: Properties: `title`, `release_year`, `description`, `imdb_id` * `User`: Properties: `user_id`, `name`, `age` * `Genre`: Properties: `genre_name` * `Actor`: Properties: `actor_name` * `Director`: Properties: `director_name` * **Edges:** * `(User)-[:WATCHED]->(Movie)`: Properties: `rating`, `timestamp` * `(Movie)-[:HAS_GENRE]->(Genre)` * `(Movie)-[:ACTED_IN]->(Actor)` * `(Movie)-[:DIRECTED_BY]->(Director)` * `(Movie)-[:SIMILAR_TO]->(Movie)`: Properties: `similarity_score` * **Example Cypher Query (Recommendation):** ```cypher MATCH (user:User {user_id: '123'})-[:WATCHED]->(m:Movie)-[:HAS_GENRE]->(g:Genre)<-[:HAS_GENRE]-(recommendedMovie:Movie) WHERE NOT (user)-[:WATCHED]->(recommendedMovie) RETURN recommendedMovie.title, recommendedMovie.description ORDER BY recommendedMovie.release_year DESC LIMIT 10 ``` * **LM Prompt Example:** "You are a graph database expert. You are working on a movie recommendation engine for a media content platform. The graph database uses Neo4j and the Cypher query language. The graph contains nodes for `User`, `Movie`, and `Genre`. Users are connected to movies they have watched with a `WATCHED` relationship. Movies are connected to genres with a `HAS_GENRE` relationship. Write a Cypher query to find movies that are in the same genre as movies that user with user_id '123' has watched, but that the user has not already watched. Return the title and description of the recommended movies, ordered by release year in descending order, and limit the results to 10." **V. Key Takeaways for LLMs** * **Relationships are Key:** Graph databases are all about relationships. Focus on understanding how entities are connected. * **Data Modeling Matters:** The graph schema is crucial. A well-designed schema will make queries easier and more efficient. * **Query Languages are Important:** Learn the basics of Cypher or Gremlin. * **MCP Context is Critical:** Understand how the graph database fits into the overall MCP architecture and how it supports the platform's goals. * **Prompt Engineering is Essential:** Use clear and concise prompts to guide the LM. By understanding these concepts, an LLM can be effectively used to assist with graph database projects in an MCP environment. This document provides a solid foundation for further learning and exploration.

mcp-server-collector MCP server

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Financial Analysis MCP Server

Máy chủ Giao thức Bối cảnh Mô hình Anthropic (MCP) để phân tích tài chính với tích hợp API từ alphavantage.com và financialmodellingprep.com.

local-command-server MCP Server

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Go Delve Debugger MCP Server

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

say-mcp-server

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Naver Maps MCP Server

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

mcp-server-curio

Máy chủ MCP được sử dụng cho dự án Curio của hệ sinh thái Filecoin.

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

Dự án này là một Máy chủ Giao thức Ngữ cảnh Mô hình (MCP) được xây dựng bằng Node.js + Express.js, tương tác với API OpenWeather. Nó cho phép người dùng lấy dữ liệu ô nhiễm không khí dựa trên vĩ độ & kinh độ hoặc thành phố & quốc gia. Phần backend đảm bảo quản lý khóa API an toàn bằng cách sử dụng dotenv và xử lý các yêu cầu API một cách hiệu quả.

@f4ww4z/mcp-mysql-server

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