Что умеет

Convert Url: Convert a web page or a publicly reachable file URL into clean, AI-ready Markdown. Call this when the user gives you a link (article, docs page, or a PDF/Office/image file URL) and wants

List Documents: List the documents in the user's LLMtoMD library, most recent first. Call this to discover what the user has stored, or to find a document_id before reading, searching, or asking about

Get Document: Fetch a stored document's full Markdown by its document_id. Use the document_id returned by convert_url or list_documents. Call this when the user wants to read, summarize, or work with

LLMtoMD is the memory layer for AI coding agents. It converts any document — PDF, DOCX, slides, spreadsheets, images, audio, even whole websites — into clean, structured Markdown, then exposes it over MCP so your agent can search your FRDs, specs, and API docs on demand instead of re-reading (or forgetting) them.

On a real 50-page spec, answering a question via retrieval used ~97% fewer tokens than holding the whole doc in context — and returned the exact, cited requirement.

Tools

This server is OAuth-secured (it serves each user's private library), so a directory's live "Tools" tab can't enumerate it without signing in — the tools are listed here instead:

list_documents / get_document — browse and read your library
search_documents — semantic search across your docs
ask_documents — RAG answer with citations
convert_url — turn any web page into Markdown
save_note — write a decision/spec back into the knowledge base
list_collections — focus on one project

Connect (OAuth — no token to paste)

Cursor / VS Code: add the server URL https://mcp.llmtomd.com/mcp
Claude Code: claude mcp add --transport http llmtomd https://mcp.llmtomd.com/mcp
Also Claude, Antigravity, and API-key for OpenAI / LangChain / LlamaIndex. Full guides: https://llmtomd.com/integrations

Free tier included (MCP on every plan) → https://llmtomd.com

llmtomd

Что умеет

Какие данные видит

Нужен ли аккаунт

Tools

Connect (OAuth — no token to paste)

convert_url	Convert a web page or a publicly reachable file URL into clean, AI-ready Markdown. Call this when the user gives you a link (article, docs page, or a PDF/Office/image file URL) and wants it as Markdown, or wants to summarize or analyze its contents. Blocks until the conversion finishes (up to ~2 minutes) and returns the Markdown plus a document_id you can reuse with the other tools. Args: url: The web page or file URL to convert. crawl: If true, follow same-origin links and convert the whole site (bounded). ocr_langs: Optional Tesseract language hint for scanned files, e.g. "eng+fra".
list_documents	List the documents in the user's LLMtoMD library, most recent first. Call this to discover what the user has stored, or to find a document_id before reading, searching, or asking about a specific document. Pass `collection` (a project name or id) to list only that project's documents.
get_document	Fetch a stored document's full Markdown by its document_id. Use the document_id returned by convert_url or list_documents. Call this when the user wants to read, summarize, or work with the full contents of a specific document.
search_documents	Semantic search across the user's LLMtoMD documents; returns the most relevant passages with similarity scores. Call this to locate where something is discussed across many documents — including specs, requirements/FRDs, and design docs when working on software. Pass document_id to search within a single document, or collection (a project name or id) to search only that project.
ask_documents	Answer a question using the user's LLMtoMD documents (retrieval-augmented): finds the relevant passages and returns a cited answer with its sources. Call this when the user asks a question whose answer lives in their stored documents — including project requirements, specs, and API references when building software, so you rely on the authoritative spec rather than guessing. Pass document_id to scope the answer to a single document, or collection (a project name or id) to scope it to one project.
list_collections	List the user's collections (projects) and how many documents each contains. A collection groups a project's documents (e.g. an FRD, specs, and design docs) so you can scope search_documents / ask_documents / list_documents to it with `collection`, or load it as context with the load_project prompt.
save_note	Save Markdown back into the user's LLMtoMD library so it persists and is searchable. Use this as write-back memory: record a decision, a spec you derived, a summary, or progress notes during a build, so the detail survives into later sessions instead of being lost when this conversation ends. Pass `collection` (a project name or id) to file the note under a project, or `append_to` (a note's document_id) to add to an existing note instead of creating a new one. Args: title: A short title for the note (used as its filename). content: The Markdown body to store. collection: Optional project to file the note under (name or id). append_to: Optional document_id of an existing note to append to.