An MCP server that enables users to fetch and audit documentation from user-defined llms.txt index files. It provides tools to list documentation sources and retrieve content from specific URLs with built-in domain access controls for secure context retrieval.
Provides controlled access to llms.txt documentation files through MCP tools, allowing AI assistants to fetch and read documentation from user-approved domains with full audit visibility of tool calls and context retrieval.
Enables MCP host applications to retrieve and process structured documentation from user-defined llms.txt files. It provides tools to fetch specific URLs and audit the documentation context returned to the LLM.
An MCP server that indexes local Python projects into a SQLite database to enable efficient symbol searching and dependency tracking. It allows users to find function or class definitions, trace module imports, and read file contents through natural language interfaces.
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 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.
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.
This MCP server provides a tool to generate manual test cases in Markdown or CSV format from documentation files and custom rules. It supports text and PDF inputs and can leverage LLM sampling to automate the creation of detailed test scenarios.
A Model Context Protocol (MCP) server that gives your AI assistant the power to convert Markdown into 14 professional document formats — PDF, DOCX, HTML, LaTeX, CSV, JSON, XML, XLSX, RTF, PNG, and more. Stop copy-pasting. Let the AI do the exporting.
Automatically transforms markdown templates (like GitHub Issue templates) into MCP tools and FastAPI endpoints. Load templates from local files, directories, or URLs to create typed API endpoints with Swagger UI documentation.
An MCP server that provides tools for analyzing, linting, formatting, and generating Markdown content. It enables users to programmatically manage Markdown files through features like table of contents generation, statistics calculation, and JSON-to-table conversion.
Headless semantic MCP server for Obsidian, Logseq, Dendron, Foam, and any markdown folder. Features built-in hybrid semantic search, surgical AST editing, template scaffolding, zero-config local embeddings, and workflow tracking.
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.
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.
Provides tools for converting Markdown content and files into professional PDF documents with full support for Mermaid diagrams and LaTeX rendering. It allows for high-quality output customization, including paper size, table of contents, and syntax highlighting styles.
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.
🧠 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.
