MCP MeasureMCP Measure

index observed

doc-convert-mcp

Document conversion MCP server: PDF to Markdown, image OCR, spreadsheet parsing.

Should I use this

Quality & Safety

A
Description quality
95%
Schema completeness
100%
Naming quality
85%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~478Tokens (tool definitions)
~1.1 KBTypical response size
Minimal attention impact (0.37% of 128k context)

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": {
    "doc-convert-mcp": {
      "url": "https://doc-convert-mcp.onrender.com/mcp/"
    }
  }
}

Remote endpoints

https://doc-convert-mcp.onrender.com/mcp/streamable-http

What it can do

Tool inventory

Tools (4)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
⚪pdf_to_markdown(pdf_base64, max_pages)

Convert a PDF (base64-encoded bytes) to Markdown, one section per page.

Input Schema

{
  "type": "object",
  "properties": {
    "pdf_base64": {
      "type": "string",
      "description": "The PDF file contents, base64-encoded."
    },
    "max_pages": {
      "default": 100,
      "type": "integer",
      "description": "Maximum pages to extract (default 100)."
    }
  },
  "required": [
    "pdf_base64"
  ],
  "additionalProperties": false
}

Output Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
⚪ocr_image(image_base64, lang)

Run OCR on an image (base64-encoded bytes) and return recognized text.

Input Schema

{
  "type": "object",
  "properties": {
    "image_base64": {
      "type": "string",
      "description": "The image file contents, base64-encoded."
    },
    "lang": {
      "default": "eng",
      "type": "string",
      "description": "Tesseract language code (default 'eng')."
    }
  },
  "required": [
    "image_base64"
  ],
  "additionalProperties": false
}

Output Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢parse_spreadsheet(file_base64, filename)

Parse a CSV or XLSX file (base64-encoded bytes) into Markdown tables.

Input Schema

{
  "type": "object",
  "properties": {
    "file_base64": {
      "type": "string",
      "description": "The spreadsheet file contents, base64-encoded."
    },
    "filename": {
      "type": "string",
      "description": "Original filename (used to detect .csv vs .xlsx)."
    }
  },
  "required": [
    "file_base64",
    "filename"
  ],
  "additionalProperties": false
}

Output Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢url_to_clean_text(url)

Fetch a public http(s) URL and return its main text content as plain text.

Input Schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "description": "The http(s) URL to fetch. Private/loopback hosts are rejected."
    }
  },
  "required": [
    "url"
  ],
  "additionalProperties": false
}

Output Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}

Community

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Evidence

Recent observations

verifiedversion not recorded4 tools