"A media conversion program (MCP) for video format conversion" matching MCP servers:

A Model Context Protocol server for converting documents between formats such as HTML, PDF, Markdown, and DOCX, built with TypeScript and designed for Claude Desktop.

Six local CPU MCP tools for Markdown/HTML-to-PDF conversion, PDF text extraction, safe SVG-to-PNG rendering, and PDF link and chapter inspection. Linux/WSL2; no GPU, hosted inference API, or model download required.

Enables document format conversion between various formats (Markdown, HTML, PDF, DOCX, LaTeX, EPUB, and more) using Pandoc, preserving formatting and structure while supporting both direct content transformation and file-based conversions.

Enables conversion between multiple document formats including Markdown, HTML, TXT, PDF, and DOCX with automatic format detection. Supports high-fidelity document transformation while preserving content integrity.

An MCP server for the PDF Tools Conversion Service that enables document conversion to PDF/A, standard PDF, merging, repair, and large file uploads.

A server that converts various file formats (PDF, images, Office documents, etc.) to Markdown descriptions using Cloudflare AI services.

Enables local document conversion between 70+ formats (PDF, Word, Excel, etc.) via MCP, keeping files private.

A professional video format conversion tool based on MCP protocol that supports multiple formats, batch processing, and quality control for video files.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables an AI agent to screen-record a live Expo app on a connected phone, auto-plan and direct the footage with captions, motion graphics, pop-outs and sound, then export a 1080p MP4. It also opens an interactive studio where users refine scenes by dragging, resizing, or typing prompts, and exposes all app_* control tools from expo-mcp-bridge in the same session.

Enables Codex to consult Claude Code for a second opinion through a local stdio MCP server, exposing the ask_claude tool for text-only reviews and recommendations using existing Claude Code authentication. Codex can request findings and tradeoff analysis without allowing Claude to edit files or run commands.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.

Enables AI coding agents to discover and verify current open-source libraries and models using live GitHub activity, PyPI adoption, license checks, and hardware VRAM estimates before generating code.

A CDP-native MCP server that lets AI agents drive a real Chrome browser with deep network/console inspection, robust page interaction, and the ability to reverse-engineer a site's API flow into runnable code with dependency chaining and replay verification.

Execution control layer for AI agents - Reserve, execute, burn/refund pattern for media generation

A live MCP server that connects AI assistants to a running Obsidian instance, enabling full app automation including reading notes, clicking UI elements, managing plugins, and executing JavaScript.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

MCP server that launches Mesen/MesenCE headlessly and exposes the emulator's debugger-face Lua API over MCP JSON-RPC, enabling AI agents to load ROMs, step frames, inspect memory/registers, manage breakpoints/watches, trace execution, and export Code/Data Logger maps for SNES, NES, PC Engine, and Game Boy Advance.

Enables compression and decompression of JSON data using the token-optimized ASON format, reducing token usage by 20-60% for LLM applications.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Enables Codex and other MCP clients to read kickoff cards, acceptance criteria, Git evidence, and next steps, then append validated, audited progress events for resumable project handoffs.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables OpenAI-compatible clients to access external tools, APIs, repositories, and data sources through MCP with allowlisting, approval, and argument validation, deployable as a FastAPI service or Python library.

Enables agents to read a project's kickoff card, acceptance criteria, AGENTS rules, handoff package, progress, Git trail, and next step, and to append validated, audited progress events with matching Git commits through short-lived scoped credentials.

Enables AI agents to retrieve Git-backed project context, handoff packages, progress, and next steps, and to append validated progress events with matching project-record commits. This supports visible agent progress, resumable work, and safe handoffs while keeping high-risk actions outside the MCP surface.