Discover Awesome MCP Servers

Gmail Server for Model Context Protocol (MCP)

Mirror of

ezmcp

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

YouTube Transcript API

Um servidor MCP ajuda a transcrever um vídeo do YouTube.

Supabase MCP Server

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JetBrains MCP Server Plugin

JetBrains MCP Server Plugin

MCPE Alpha Server for Pterodactyl

Núcleos para servidores MCPE Alpha usando o painel Pterodactyl.

mcp-server-kubernetes

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mcp-github-server

GitHub APIを使用したModel Context Protocolサーバーの実装

Letta MCP Server

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testmcpgithubdemo

created from MCP server demo

ChEMBL-MCP-Server

🔍 Habilite assistentes de IA para pesquisar, acessar e analisar o ChEMBL através de uma interface MCP simples.

duckduckgo-web-search MCP Server

DuckDuckGo Web Search MCP Server - A simple web search implementation for Claude Desktop using DuckDuckGo API

Mcp Ffmpeg

Model Context Protocol Server to facilitate llms to able to decode, encode, transcode, mux, demux, stream, filter and play pretty much anything that humans and machines have created.

Ip Mcp Server

A IP MCP server.

MCP TTS Say

MCP Server Tool for Text To Speech

Awesome-Medical-MCP-Servers

Uma coleção de servidores médicos MCP.

CLI MCP Server

Mirror of

graphexpert2025

Okay, here are comprehensive notes on Graph Databases and MCP (presumably Minecraft Protocol) Servers, designed to provide enough context for a Language Model (LM) to quickly understand and contribute to Graph Database projects related to MCP. I've broken it down into sections for clarity. **I. Graph Databases: Core Concepts** * **What are they?** Graph databases are a type of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store data. They excel at managing and querying relationships between data points. * **Key Components:** * **Nodes (Vertices):** Represent entities (people, places, things, events). Think of them as the nouns in your data. * **Edges (Relationships):** Represent connections between nodes. They have a direction (directed graph) or no direction (undirected graph). Think of them as the verbs in your data. Edges can also have properties. * **Properties:** Key-value pairs that describe nodes and edges. For example, a "Person" node might have properties like "name," "age," and "location." An "IS_FRIENDS_WITH" edge might have a property like "since" (date). * **Why Use Graph Databases?** * **Relationship-Centric Data:** Ideal for data where relationships are as important as the data itself. Examples: social networks, recommendation engines, knowledge graphs, fraud detection, network management, and, relevant to MCP, game world relationships. * **Complex Queries:** Efficiently handle complex queries that traverse multiple relationships. Traditional relational databases often struggle with these types of queries, requiring complex JOINs. * **Flexibility:** Easily adapt to changing data models. Adding new relationships or properties is generally less disruptive than in relational databases. * **Performance:** Graph databases are optimized for traversing relationships, leading to faster query performance for relationship-heavy data. * **Common Graph Database Technologies:** * **Neo4j:** A popular, mature, and widely used graph database. Supports Cypher query language. * **Amazon Neptune:** A fully managed graph database service from AWS. Supports both Gremlin and SPARQL query languages. * **JanusGraph:** A distributed, scalable graph database. Supports Gremlin query language. * **TigerGraph:** A high-performance graph database designed for complex analytics. Uses GSQL query language. * **Dgraph:** A distributed, fast, and scalable graph database. Uses GraphQL-like query language. * **Graph Query Languages:** * **Cypher (Neo4j):** A declarative graph query language designed to be easy to read and write. Uses an ASCII-art-like syntax to represent graph patterns. Example: `MATCH (a:Person)-[:IS_FRIENDS_WITH]->(b:Person) WHERE a.name = "Alice" RETURN b` (Find Alice's friends). * **Gremlin (Apache TinkerPop):** A graph traversal language that can be used with various graph databases. Uses a functional, chained syntax. Example: `g.V().has('name', 'Alice').out('IS_FRIENDS_WITH').values('name')` (Find Alice's friends). * **SPARQL:** A query language for RDF (Resource Description Framework) data, often used with knowledge graphs. * **GSQL (TigerGraph):** A SQL-like query language designed for parallel graph processing. * **Graph Data Modeling:** * **Identify Entities:** Determine the key entities in your domain and represent them as nodes. * **Define Relationships:** Identify the relationships between entities and represent them as edges. Consider the directionality and properties of the relationships. * **Choose Properties:** Determine the relevant properties for nodes and edges. * **Consider Constraints:** Think about any constraints on the data, such as uniqueness or required properties. **II. Minecraft Protocol (MCP) Servers: Context for Graph DB Applications** * **What is MCP?** The Minecraft Protocol (MCP) is the communication protocol used between Minecraft clients and servers. It defines how clients send actions (e.g., moving, breaking blocks, chatting) to the server and how the server sends updates (e.g., world changes, player positions, chat messages) back to the clients. * **Why is it relevant to Graph Databases?** MCP data can be used to build a graph representation of the Minecraft world and player interactions. This graph can then be used for various purposes, such as: * **Social Network Analysis:** Analyzing player interactions (chat, trading, proximity) to understand social dynamics within the game. * **World Exploration and Discovery:** Mapping the game world, identifying points of interest, and tracking player exploration patterns. * **Resource Management:** Tracking resource usage, identifying bottlenecks, and optimizing resource allocation. * **Anomaly Detection:** Identifying unusual player behavior that might indicate cheating or griefing. * **Game AI and Pathfinding:** Building a graph representation of the world for AI agents to navigate and interact with. * **Modding and Plugin Development:** Providing a structured way to access and analyze game data for modding and plugin development. * **Key Data Points from MCP for Graph DBs:** * **Players:** Represented as nodes with properties like username, UUID, location, inventory, health, etc. * **Entities (Mobs, Items, etc.):** Represented as nodes with properties like type, location, health, owner (if applicable), etc. * **Blocks:** Represented as nodes with properties like type, location, material, etc. Consider chunk-based aggregation for scalability. * **Chunks:** Represented as nodes, containing references to the blocks within them. * **Relationships:** * `PLAYER_NEAR_PLAYER`: Connects players who are in close proximity. Properties: distance, timestamp. * `PLAYER_INTERACTED_WITH_PLAYER`: Connects players who have interacted (e.g., traded, attacked). Properties: type of interaction, timestamp. * `PLAYER_BROKE_BLOCK`: Connects a player to a block they broke. Properties: timestamp, tool used. * `PLAYER_PLACED_BLOCK`: Connects a player to a block they placed. Properties: timestamp, block type. * `PLAYER_OWNS_ITEM`: Connects a player to an item in their inventory. Properties: quantity. * `BLOCK_IS_PART_OF_CHUNK`: Connects a block to the chunk it belongs to. * `CHUNK_IS_ADJACENT_TO_CHUNK`: Connects adjacent chunks. * `ENTITY_IS_IN_CHUNK`: Connects an entity to the chunk it is in. * `ENTITY_ATTACKED_ENTITY`: Connects two entities, with the attacker and the attacked. Properties: damage, timestamp. * **Challenges:** * **Data Volume:** Minecraft worlds can be very large, generating a massive amount of data. Scalability is a key concern. * **Real-time Updates:** The game world is constantly changing, requiring real-time or near real-time updates to the graph database. * **Data Complexity:** The MCP data is complex and requires careful parsing and processing. * **Performance Optimization:** Querying large graphs can be slow if not optimized properly. Consider indexing and caching strategies. * **Version Compatibility:** The Minecraft protocol changes with each version, requiring updates to the data ingestion pipeline. **III. Putting it Together: Example Project Scenarios** * **Social Network Analysis:** * **Goal:** Identify influential players in a Minecraft server. * **Graph Model:** Players as nodes, `PLAYER_INTERACTED_WITH_PLAYER` edges. * **Queries:** Find players with the most connections, identify communities of players who frequently interact. * **Technology:** Neo4j with Cypher. * **World Exploration and Discovery:** * **Goal:** Create a map of the most frequently visited locations in a Minecraft world. * **Graph Model:** Blocks/Chunks as nodes, `CHUNK_IS_ADJACENT_TO_CHUNK` edges, `PLAYER_VISITED_BLOCK` edges. * **Queries:** Find the most connected blocks/chunks, identify paths between locations. * **Technology:** JanusGraph with Gremlin (for scalability). * **Anomaly Detection:** * **Goal:** Identify players who are teleporting or using other cheats. * **Graph Model:** Players as nodes, `PLAYER_MOVED_TO_LOCATION` edges (representing player movement). * **Queries:** Detect sudden jumps in location that are not possible through normal movement. * **Technology:** A real-time graph database like Dgraph. **IV. Considerations for Language Models (LMs)** * **Code Generation:** LMs can be used to generate graph queries (Cypher, Gremlin, etc.) based on natural language descriptions of the desired data. Provide the LM with examples of queries and their corresponding natural language descriptions. * **Data Modeling Assistance:** LMs can assist in designing the graph data model by suggesting entities, relationships, and properties based on a description of the Minecraft world and the desired analysis. * **Documentation Generation:** LMs can generate documentation for the graph database schema, queries, and data ingestion pipeline. * **Error Detection:** LMs can be trained to identify potential errors in graph queries or data models. * **Contextual Understanding:** The LM needs to understand the specific context of the Minecraft world and the MCP protocol to generate meaningful queries and insights. Provide the LM with relevant documentation and examples. **V. Key Terms and Acronyms** * **MCP:** Minecraft Protocol * **NoSQL:** Not Only SQL (a type of database that doesn't use the relational model) * **UUID:** Universally Unique Identifier * **API:** Application Programming Interface * **RDF:** Resource Description Framework * **GSQL:** Graph SQL (TigerGraph's query language) **In Summary:** This document provides a foundation for understanding graph databases in the context of Minecraft Protocol (MCP) data. By understanding the core concepts of graph databases, the relevant data points from MCP, and the potential applications, a Language Model can be effectively used to contribute to graph database projects related to Minecraft. Remember to focus on the relationships between entities in the Minecraft world and how those relationships can be represented and queried using a graph database. Good luck!

Selector_MCP_Server

Selector AI MCP Server

Model Context Protocol Server

Implementação de um servidor de Protocolo de Contexto de Modelo com FastAPI

Setup

An MCP Server for Ableton Live

mcp-server-collector MCP server

Mirror of

Laravel Artisan MCP Server

Um servidor de Protocolo de Contexto de Modelo (MCP) que permite a execução segura de comandos Laravel Artisan através do Claude e outros clientes MCP.

sek-fx-mcp

Um servidor de Protocolo de Contexto de Modelo (MCP) que conecta um LLM à API do Riksbank (Banco Central da Suécia) para a taxa de câmbio da coroa 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 projeto é um Servidor de Protocolo de Contexto de Modelo (MCP) construído usando Node.js + Express.js que interage com a API OpenWeather. Ele permite que os usuários busquem dados de poluição do ar com base na latitude e longitude ou cidade e país. O backend garante o gerenciamento seguro da chave da API usando dotenv e lida com as solicitações da API de forma eficiente.