Repository hosting for the MCP-PDF2MD service, allowing users to clone and set up the service from GitHub
Automatically recognizes and converts mathematical formulas found in PDF documents to LaTeX format
Converts PDF content to structured Markdown format, preserving document structure including headings, paragraphs, lists, and tables
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-pdf2md convert this PDF file to markdown"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP-based high-performance PDF to Markdown conversion service powered by MinerU API, supporting batch processing for local files and URL links with structured output.
Format Conversion: Convert PDF files to structured Markdown format.
Multi-source Support: Process both local PDF files and URL links.
Intelligent Processing: Automatically select the best processing method.
Batch Processing: Support multi-file batch conversion for efficient handling of large volumes of PDF files.
MCP Integration: Seamless integration with LLM clients like Claude Desktop.
Structure Preservation: Maintain the original document structure, including headings, paragraphs, lists, etc.
Smart Layout: Output text in human-readable order, suitable for single-column, multi-column, and complex layouts.
Formula Conversion: Automatically recognize and convert formulas in the document to LaTeX format.
Table Extraction: Automatically recognize and convert tables in the document to structured format.
Cleanup Optimization: Remove headers, footers, footnotes, page numbers, etc., to ensure semantic coherence.
High-Quality Extraction: High-quality extraction of text, images, and layout information from PDF documents.
Related MCP server: pdf2md-mcp
Software: Python 3.10+
Clone the repository and enter the directory:
git clone https://github.com/FutureUnreal/mcp-pdf2md.git
cd mcp-pdf2mdCreate a virtual environment and install dependencies:
Linux/macOS:
uv venv
source .venv/bin/activate
uv pip install -e .Windows:
uv venv
.venv\Scripts\activate
uv pip install -e .Configure environment variables:
Create a .env file in the project root directory and set the following environment variables:
MINERU_API_BASE=https://mineru.net/api/v4/extract/task
MINERU_BATCH_API=https://mineru.net/api/v4/extract/task/batch
MINERU_BATCH_RESULTS_API=https://mineru.net/api/v4/extract-results/batch
MINERU_API_KEY=your_api_key_hereStart the service:
uv run pdf2mdThe server supports the following command line arguments:
Add the following configuration in Claude Desktop:
Windows:
{
"mcpServers": {
"pdf2md": {
"command": "uv",
"args": [
"--directory",
"C:\\path\\to\\mcp-pdf2md",
"run",
"pdf2md",
"--output-dir",
"C:\\path\\to\\output"
],
"env": {
"MINERU_API_KEY": "your_api_key_here"
}
}
}
}Linux/macOS:
{
"mcpServers": {
"pdf2md": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-pdf2md",
"run",
"pdf2md",
"--output-dir",
"/path/to/output"
],
"env": {
"MINERU_API_KEY": "your_api_key_here"
}
}
}
}Note about API Key Configuration: You can set the API key in two ways:
In the .env file within the project directory (recommended for development)
In the Claude Desktop configuration as shown above (recommended for regular use)
If you set the API key in both places, the one in the Claude Desktop configuration will take precedence.
The server provides the following MCP tools:
convert_pdf_url: Convert PDF URL to Markdown
convert_pdf_file: Convert local PDF file to Markdown
This project relies on the MinerU API for PDF content extraction. To obtain an API key:
Visit MinerU official website and register for an account
After logging in, apply for API testing qualification at this link
Once your application is approved, you can access the API Management page
Generate your API key following the instructions provided
Copy the generated API key
Use this string as the value for MINERU_API_KEY
Note that access to the MinerU API is currently in testing phase and requires approval from the MinerU team. The approval process may take some time, so plan accordingly.
MIT License - see the LICENSE file for details.
This project is based on the API from MinerU.
Convert local PDF file to Markdown, supports single file or file list
Args:
file_path: PDF file local path or path list, can be separated by spaces, commas, or newlines
enable_ocr: Whether to enable OCR (default: True)
Returns:
dict: Conversion result information
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | ||
| enable_ocr | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the OCR capability and return format (dict with conversion result information), but lacks critical details: whether this is a read-only operation, what happens with invalid files, if there are size/time limitations, what specific information the result dict contains, or error handling behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise with clear sections (Args, Returns) and front-loaded purpose statement. However, the 'Args' and 'Returns' labels add some redundancy since this information is partially available in the schema, and some sentences could be more efficiently worded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a file conversion tool with 2 parameters, no annotations, and no output schema, the description is insufficient. It lacks information about file format requirements, conversion quality, error conditions, output structure details, performance characteristics, or how the tool differs from its sibling. The return value description ('dict: Conversion result information') is particularly vague given no output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides basic parameter information in the Args section, explaining that file_path accepts local paths or lists with various separators, and enable_ocr defaults to True. However, with 0% schema description coverage, it doesn't fully compensate by explaining path format requirements, file accessibility constraints, or what OCR actually does in this context beyond the boolean toggle.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: converting PDF files to Markdown format, with support for single files or lists. It specifies the resource (PDF files) and action (convert to Markdown), though it doesn't explicitly differentiate from the sibling tool 'convert_pdf_url' which likely handles URL-based PDFs rather than local files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions support for single files or lists, it doesn't explain when to choose this over 'convert_pdf_url' or other potential conversion tools. There's no mention of prerequisites, limitations, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert PDF URL to Markdown, supports single URL or URL list
Args:
url: PDF file URL or URL list, can be separated by spaces, commas, or newlines
enable_ocr: Whether to enable OCR (default: True)
Returns:
dict: Conversion result information
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| enable_ocr | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions OCR support with a default setting, which adds some context, but fails to describe critical behaviors such as rate limits, authentication requirements, error handling, or what the conversion result information includes. For a tool that processes external URLs and performs conversion, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by parameter details in a structured format. Every sentence adds value, with no redundant information. However, the use of 'dict' in the returns section is slightly vague, though this is mitigated by the lack of an output schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (processing PDF URLs with OCR options) and the absence of annotations and output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks details on behavioral traits, error cases, and output structure. This leaves gaps that could hinder an agent's ability to use the tool effectively in varied contexts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'url' can be a single URL or a list separated by spaces, commas, or newlines, and clarifies the default value and purpose of 'enable_ocr'. This compensates well for the schema's lack of descriptions, making the parameters understandable without relying on the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: converting PDF URLs to Markdown format. It specifies the resource (PDF URLs) and the action (convert to Markdown), which is specific and actionable. However, it doesn't explicitly differentiate from its sibling tool 'convert_pdf_file' beyond mentioning URL vs. file handling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning support for single URLs or URL lists, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'convert_pdf_file'. No when-not-to-use scenarios or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
convert_pdf_fileconvert_pdf_urlThe two tools have clearly distinct purposes: one handles local file paths, the other handles URLs. The naming and descriptions make it impossible to confuse which tool to use for a given input source.
Both tools follow an identical verb_noun pattern (convert_pdf_file and convert_pdf_url) with consistent snake_case formatting. The naming is perfectly predictable across the toolset.
With only two tools, the server feels minimal but functional. While it covers the core conversion task for both local files and URLs, the count is borderline thin for a PDF-to-Markdown domain that could potentially include more operations like batch processing, format options, or metadata extraction.
The server covers the essential conversion operation for both local files and remote URLs, which are the two main input sources for PDFs. The minor gap is the lack of additional PDF manipulation or output customization tools, but agents can perform basic conversions without dead ends.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Convert documents and web pages to clean Markdown: PDF, DOCX, XLSX, EPUB, scanned files, any URL.
High-fidelity PDF to structured Markdown conversion and document field extraction.
Convert PDF, DOCX, HTML, and URLs to clean, LLM-ready markdown with tables preserved
PDF accessibility checks (veraPDF PDF/UA-1), auto-fix and Markdown conversion. EU-hosted.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/FutureUnreal/mcp-pdf2md'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
