"Tools for Converting LaTeX Mathematics to Lean Formalizations" matching MCP servers:

A comprehensive MCP server that turns any AI assistant into a powerful mathematical computation engine, providing 52 advanced functions, 158 unit conversions, financial calculations, and secure AST-based evaluation.

MCP server that converts URLs into token-minimized clean text for LLMs, providing a receipt of token and cost savings.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

An MCP server for LaTeX that enables AI assistants to check documents for errors, parse compiler logs, and compile LaTeX files, helping them fix documents instead of guessing.

Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.

Enables compiling Lean 4 proof snippets on the local machine against a Mathlib project and returns structured compiler feedback — status, one-line hints, and the compiler's real error, warning, and information messages — so a model can iterate on proofs instead of guessing. Compilation is serialized, timeouts kill stuck tactics, and rejected code is reported as a normal result rather than a tool failure.

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

An MCP server that enables MCP clients to directly access and process arXiv papers by fetching the LaTeX source, improving mathematical content understanding.

A self-hosted MCP server for Linear that reduces response sizes by field-selecting minimal data, saving tokens and costs in LLM agent sessions.

Enables AI assistants to create, edit, and validate LaTeX documents through a standardized protocol with support for multiple document classes and package management. It provides tools for document structure analysis and file organization to streamline the generation of professional academic papers.

Enables low-cost agent models to control Windows applications through a compact, state-safe proxy over Open Computer Use, reducing model-visible context by up to 99.8% with support for record/replay and reusable UI component memory.

Enables deterministic calculations for AI API and subscription costs, meeting time zones, recurring schedules, and cron next-run previews, returning normalized inputs, units, assumptions, dated sources, and links to results.

Enables agents to convert Markdown into WhatsApp-compatible formatting, including styled text, lists, and tables that fit a phone's monospace width.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

MCP server for personal knowledge management providing AI-powered tools for semantic search, audio transcription, image generation, and PPT creation.

Enables AI assistants to convert CSS-animated SVGs into videos (MP4/WebM/MKV/MOV) and animated images (aPNG/GIF) with frame-accurate rendering and transparency support.

MCP server that exposes tools to route natural language queries to typed IDs, describe catalog entries, and list repository verification commands for the m-dev-tools organization.

Provides five safe tools (calc, search, model_drift, compare_runs, grade_answer) for arithmetic, keyword search, model drift monitoring, eval comparison, and answer grading against sources, all implemented directly from the MCP spec with zero dependencies.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.