Enables task management through a local MySQL database, supporting full CRUD operations and automated tracking of status transitions. Users can create, search, and update tasks while maintaining a detailed progress history for all activities.
A system that localizes Model Context Protocol (MCP) functionality, integrating AI management through CPU and RAM without requiring a GPU.
A local development MCP server that exposes MySQL databases to VSCode and Copilot CLI with read-only SELECT queries and INSERT/UPDATE operations. It provides secure, schema-specific database access for development environments only.
Enables direct interaction with local WordPress development sites through Local by Flywheel database connections. Provides read-only access to WordPress data including posts, users, options, and custom queries for development and analysis.
MCP servers are installed via npx -y @scope/package — which silently downloads the latest version every time your AI tool starts, with no integrity check. mcp-lock fixes this by recording exact tarball hashes on first run and detecting any changes on every run after that — the same guarantee npm ci gives you for Node.js projects.
A personal fitness tracking server that enables logging and querying workouts, nutrition, and body metrics through a local SQLite database. Integrates with OpenNutrition MCP for food logging and supports exercise history tracking for workout progression.
Enables first-order logic reasoning including theorem proving, model finding, counterexample detection, and category theory diagram verification using pure TypeScript with no external dependencies.
Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.
An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.
Enables AI assistants to interact with MariaDB databases through SQL operations and vector-based semantic search. Supports standard database queries, schema inspection, and optional embedding-powered document storage and retrieval.
MCP server for the Russian construction market — 3,395 contractor companies and 13,436 house-building projects across 18 regions. 21 tools for search, comparison, cost analytics, contractor recommendations, and quote requests.
Provides AI assistants with access to materials science databases, enabling search and analysis of material properties, crystal structures, phase diagrams, and elastic properties through the Materials Project API.
A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.
A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and getting weather data to demonstrate basic functionality and API integration patterns.
MCP server that routes medical questions and images to MedGemma models hosted on Vertex AI, enabling interaction with text-only and multimodal medical AI systems.
Provides AI coding assistants with persistent memory storage using a local SQLite database. Enables tools to remember project details, notes, and relationships across sessions to maintain context and reduce repetitive explanations.
Enables AI assistants to store and retrieve memories with semantic search capabilities using vector embeddings. Provides persistent memory storage with SQLite backend for context retention across conversations.
🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.
Enables AI assistants to store and retrieve long-term memories using PostgreSQL with vector similarity search. Supports semantic memory operations, tagging, and real-time updates for persistent learning across conversations.
A persistent local vector memory server that allows users to store and search project-specific context using LanceDB and local embeddings. It enables MCP-compliant editors to maintain long-term memory across different projects without requiring external API keys.
