"How to generate a PDF from a Markdown file" matching MCP servers:

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables AI to search, retrieve, list notes, and get backlinks from a published Markdown vault (Obsidian) via GitHub Pages.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A local RAG MCP server for PDF development experience, enabling document ingestion, semantic search, and Q\&A with source citations using TF-IDF and cosine similarity.

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.

Python MCP server for programmatic access to markdown-based knowledge vaults, enabling AI assistants to browse, read, search, update, and manage notes, tasks, and projects.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

Enables AI assistants to query and interact with a graph database of markdown notes, extracting entities like wikilinks, mentions, and hashtags.

Enables querying Markdown files in knowledge bases (like Obsidian vaults) by front matter metadata, filtering notes by tags, recency, and folders to surface relevant content based on created/updated dates.

Enables AI agents to maintain a structured Markdown or Obsidian memory vault with tools for reading, writing, searching, and organizing notes.

Enables AI agents to extract PDF text, search with PDF++ citations, access vault files, manage tags, and run Dataview queries in Obsidian via MCP.

Enables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.

MCP bridge for PDF Content Search — full-text PDF search with Apple Vision OCR across thousands of documents in under a second from Claude, Cursor, or any MCP client. Advanced filters (date, category, sender, amount), wildcards, boolean operators. Bridge open-source (MIT), PDF Content Search app is commercial with free iOS+Android companion scanner apps.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables AI agents to persist all generated deliverables as structured Markdown in a project's /.aiartefact directory, with automatic frontmatter, metadata indexing, and full-text search.