inktomd

endpoint

https://mcp.inktomd.com/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is inktomd live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is inktomd free to use?

Yes — the hub reached it with no key and no payment.

What tools does inktomd have?

9 tools: convert_url, convert_file, convert_youtube, convert_arxiv, convert_batch, count_tokens, convert_with_metadata, prepare_for_rag, ….

Is inktomd safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• list_supported_formats reads open 6h ago

  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.

  mcp-tool

  12345

  {
    "type": "object",
    "title": "list_supported_formatsArguments",
    "properties": {}
  }

  arguments 5 lines copy
• convert_url reads unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "title": "convert_urlArguments",
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "title": "Url"
      }
    }
  }

  arguments 13 lines copy
• convert_file reads unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "title": "convert_fileArguments",
    "required": [
      "file_path"
    ],
    "properties": {
      "file_path": {
        "type": "string",
        "title": "File Path"
      }
    }
  }

  arguments 13 lines copy
• convert_youtube reads unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "title": "convert_youtubeArguments",
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "title": "Url"
      }
    }
  }

  arguments 13 lines copy
• convert_arxiv reads unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "title": "convert_arxivArguments",
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "title": "Url"
      }
    }
  }

  arguments 13 lines copy
• convert_batch reads unknown never probed

  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

  mcp-tool

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  {
    "type": "object",
    "title": "convert_batchArguments",
    "required": [
      "urls"
    ],
    "properties": {
      "urls": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "title": "Urls"
      }
    }
  }

  arguments 16 lines copy
• count_tokens reads unknown 6h ago

  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

  mcp-tool

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  {
    "type": "object",
    "title": "count_tokensArguments",
    "required": [
      "text"
    ],
    "properties": {
      "text": {
        "type": "string",
        "title": "Text"
      },
      "model": {
        "type": "string",
        "title": "Model",
        "default": "gpt-4o"
      }
    }
  }

  arguments 18 lines copy
• convert_with_metadata reads unknown never probed

  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

  mcp-tool

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  {
    "type": "object",
    "title": "convert_with_metadataArguments",
    "required": [
      "source"
    ],
    "properties": {
      "source": {
        "type": "string",
        "title": "Source"
      },
      "source_type": {
        "type": "string",
        "title": "Source Type",
        "default": "url"
      }
    }
  }

  arguments 18 lines copy
• prepare_for_rag reads unknown never probed

  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

  mcp-tool

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  {
    "type": "object",
    "title": "prepare_for_ragArguments",
    "required": [
      "source"
    ],
    "properties": {
      "source": {
        "type": "string",
        "title": "Source"
      },
      "overlap": {
        "type": "integer",
        "title": "Overlap",
        "default": 50
      },
      "chunk_size": {
        "type": "integer",
        "title": "Chunk Size",
        "default": 512
      },
      "source_type": {
        "type": "string",
        "title": "Source Type",
        "default": "url"
      }
    }
  }

  arguments 28 lines copy

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