docling-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DOCLING_MCP_HOST | No | HTTP host | 127.0.0.1 |
| DOCLING_MCP_PORT | No | HTTP port | 8765 |
| DOCLING_MCP_VLM_URL | No | OpenAI-compatible chat completions URL | |
| DOCLING_MCP_OCR_LANGS | No | Comma-separated OCR languages | en |
| DOCLING_MCP_TRANSPORT | No | Transport mode: 'stdio' or 'http' | stdio |
| DOCLING_MCP_VLM_MODEL | No | VLM model name | gpt-4o-mini |
| DOCLING_MCP_HF_ENDPOINT | No | HuggingFace mirror endpoint, e.g. https://hf-mirror.com for China | |
| DOCLING_MCP_MAX_FILE_MB | No | Per-file size cap in MB | 200 |
| DOCLING_MCP_VLM_API_KEY | No | VLM bearer key | |
| DOCLING_MCP_VLM_ENABLED | No | Global VLM default (true/false) | false |
| DOCLING_MCP_HF_BYPASS_PROXY | No | Set to 'true' to drop local proxy env vars before HF imports | true |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| convert_to_markdownA | Convert a document or image to Markdown. Supports PDF, DOCX, PPTX, HTML, and image files (PNG/JPG/etc.). Args: source: Local path, HTTP(S) URL, or data URI (data:;base64,...). ocr_languages: Override default OCR languages, e.g. ["en","zh"]. Defaults to env. enable_vlm: If True and a VLM endpoint is configured, generate richer image descriptions via an OpenAI-compatible API. Falls back to OCR text if unavailable. page_range: Optional (start, end) 1-indexed inclusive page range for PDFs. image_caption_mode: How to represent pictures in markdown: - "skip": placeholder only - "ocr_text": picture's OCR'd text (default) - "vlm_description": VLM-generated description (requires enable_vlm) Returns: ConvertMarkdownResult as dict: {markdown, num_pages, num_tables, num_pictures, source_type, warnings}. |
| convert_to_textA | Convert a document or image to plain text (no markdown formatting). Best for feeding token-limited text-only LLMs. Tables are linearized. Returns just the text string. |
| extract_tablesA | Extract all tables from a document. Returns a list of {page, index, num_rows, num_cols, markdown, rows}.
Each |
| chunk_for_ragA | Chunk a document for RAG using docling's HybridChunker. Returns list of {text, index, page, headings, chunk_type, token_count}. Args: source: Local path / URL / data URI. chunk_size: Target max tokens per chunk. overlap: Overlap tokens between adjacent chunks. tokenizer: HuggingFace tokenizer name. Defaults to BGE-small (English). Use a multilingual tokenizer (e.g. "bert-base-multilingual-cased") for non-English docs. ocr_languages: Override default OCR languages. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: plain text conversion, markdown conversion, table extraction, and RAG chunking. No two tools overlap in function, and the descriptions clarify the differences even between the two conversion tools.
All tool names follow a consistent verb_noun pattern in snake_case (convert_to_text, convert_to_markdown, extract_tables, chunk_for_rag). The naming is uniform and predictable.
With 4 tools, the server is well-scoped for document processing. Each tool covers a distinct need without redundancy, and the number is within the ideal range.
The server covers the primary conversion and extraction needs (text, markdown, tables) plus chunking for RAG. Minor gaps exist, such as direct document structure extraction or image extraction, but agents can work around these using the provided tools.