mcp-docparser
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| parse_documentC | Parse any document including images with OCR (PDF, Word, Excel, Images, etc.) |
| extract_textB | Extract text from any document or image using OCR if needed |
| ocr_imageC | Extract text from image using OCR (supports Chinese and English) |
| analyze_imageC | Analyze image content, detect text presence, and get metadata |
| batch_ocrC | Process multiple images in a folder and extract text |
| chunk_documentC | Split document or extracted text into chunks for processing |
| get_document_infoC | Get metadata and structure information about any document or image |
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 7 tools
Multiple tools have overlapping purposes: parse_document, extract_text, and ocr_image all extract text from images/documents, with descriptions that are nearly indistinguishable. analyze_image also overlaps with get_document_info for image metadata, making it hard for an agent to choose the right tool.
All tool names use snake_case and most follow a verb_noun pattern (e.g., analyze_image, chunk_document, get_document_info). batch_ocr and ocr_image deviate slightly by starting with a noun or acronym, but the overall convention is clear and readable.
Seven tools is well-scoped for a document parsing server, providing distinct capabilities for parsing, OCR, text extraction, metadata, image analysis, and chunking without being overwhelming or thin.
The surface covers core document parsing tasks: parsing, text extraction, OCR (including batch and language support), metadata retrieval, image analysis, and chunking. Minor gaps exist, such as tools for output saving or format-specific handling, but agents can work around them.