inktomd MCP Server
Convert files, URLs, and documents to clean, AI-ready Markdown via MCP.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"inktomd-mcp": {
"url": "https://mcp.inktomd.com/mcp"
}
}
}Remote endpoints
https://mcp.inktomd.com/mcpstreamable-httpWhat it can do
Tool inventory
Tools (9)
⚪convert_url(url)
Convert any URL to clean AI-ready Markdown. Supports webpages, YouTube videos, ArXiv papers, Wikipedia articles, Substack newsletters, RSS feeds, Google Docs, GitHub pages, and more. Returns Markdown with up to 63% fewer tokens than the raw source HTML.
Input Schema
{
"type": "object",
"properties": {
"url": {
"title": "Url",
"type": "string"
}
},
"required": [
"url"
],
"title": "convert_urlArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "convert_urlOutput"
}⚪convert_file(file_path)
Convert a local file to clean AI-ready Markdown. Supports PDF, Word (.doc/.docx), Excel (.xls/.xlsx), PowerPoint (.ppt/.pptx), EPUB, HTML, CSV, JSON, XML, Jupyter notebooks (.ipynb), Email files (.eml/.msg), ZIP archives (.zip), and 7-Zip archives (.7z). Provide the absolute file path. Maximum file size: 20MB.
Input Schema
{
"type": "object",
"properties": {
"file_path": {
"title": "File Path",
"type": "string"
}
},
"required": [
"file_path"
],
"title": "convert_fileArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "convert_fileOutput"
}🟢convert_youtube(url)
Extract the full transcript from any public YouTube video as clean Markdown. Works with standard watch links (youtube.com/watch?v=) and short links (youtu.be/). The video must have captions enabled — including auto-generated captions. Returns the transcript as flowing Markdown paragraphs, not raw caption fragments.
Input Schema
{
"type": "object",
"properties": {
"url": {
"title": "Url",
"type": "string"
}
},
"required": [
"url"
],
"title": "convert_youtubeArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "convert_youtubeOutput"
}⚪convert_arxiv(url)
Convert any ArXiv research paper to clean structured Markdown. Accepts both abstract page URLs (arxiv.org/abs/PAPER_ID) and direct PDF links (arxiv.org/pdf/PAPER_ID). Returns the full paper content with headings, sections, and content preserved — uses significantly fewer tokens than the PDF format for AI analysis.
Input Schema
{
"type": "object",
"properties": {
"url": {
"title": "Url",
"type": "string"
}
},
"required": [
"url"
],
"title": "convert_arxivArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "convert_arxivOutput"
}🟢list_supported_formats
List all file formats and URL types that inktomd supports for conversion to Markdown. Use this to check whether a specific file type or URL source is supported before attempting conversion.
Input Schema
{
"type": "object",
"properties": {},
"title": "list_supported_formatsArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "list_supported_formatsOutput"
}🟢count_tokens(text, model)
Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status
Input Schema
{
"type": "object",
"properties": {
"text": {
"title": "Text",
"type": "string"
},
"model": {
"default": "gpt-4o",
"title": "Model",
"type": "string"
}
},
"required": [
"text"
],
"title": "count_tokensArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "count_tokensOutput"
}🟢convert_batch(urls)
Convert multiple URLs to Markdown in a single call. Maximum 10 URLs per batch. Each URL is converted independently. Args: urls: List of URLs to convert. Maximum 10. Each must start with http:// or https:// Returns: All converted Markdown documents combined, clearly separated with headers
Input Schema
{
"type": "object",
"properties": {
"urls": {
"items": {
"type": "string"
},
"title": "Urls",
"type": "array"
}
},
"required": [
"urls"
],
"title": "convert_batchArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "convert_batchOutput"
}⚪convert_with_metadata(source, source_type)
Convert a file or URL to Markdown and return both content and structured metadata. Metadata includes title, estimated token counts for all major models, word count, character count, and reading time. Args: source: Either a URL (starting with http/https) or absolute file path source_type: Either "url" or "file". Default: "url" Returns: Markdown content with a metadata header block containing all stats
Input Schema
{
"type": "object",
"properties": {
"source": {
"title": "Source",
"type": "string"
},
"source_type": {
"default": "url",
"title": "Source Type",
"type": "string"
}
},
"required": [
"source"
],
"title": "convert_with_metadataArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "convert_with_metadataOutput"
}🟢prepare_for_rag(source, source_type, chunk_size, overlap)
Convert a file or URL to Markdown, then split it into optimally-sized chunks ready for insertion into a vector database or RAG pipeline. Returns a JSON array of chunks with token counts, making this the single tool needed to go from raw document to RAG-ready data. Args: source: Either a URL (starting with http/https) or absolute file path source_type: Either "url" or "file". Default: "url" chunk_size: Target token count per chunk. Default: 512. Recommended range: 256-1024 overlap: Token overlap between consecutive chunks to preserve context. Default: 50 Returns: JSON array of chunks, each with: chunk_id, text, token_count, char_count
Input Schema
{
"type": "object",
"properties": {
"source": {
"title": "Source",
"type": "string"
},
"source_type": {
"default": "url",
"title": "Source Type",
"type": "string"
},
"chunk_size": {
"default": 512,
"title": "Chunk Size",
"type": "integer"
},
"overlap": {
"default": 50,
"title": "Overlap",
"type": "integer"
}
},
"required": [
"source"
],
"title": "prepare_for_ragArguments"
}Output Schema
{
"type": "object",
"properties": {
"result": {
"title": "Result",
"type": "string"
}
},
"required": [
"result"
],
"title": "prepare_for_ragOutput"
}Community
Evidence