paddleocr-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PADDLEOCR_DEVICE | No | Force device: 'cpu' or 'gpu' | auto |
| PADDLEOCR_VL_TIMEOUT | No | VL-1.6 timeout in seconds | 300 |
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 |
|---|---|
| ocr_imageC | Run the latest local PaddleOCR text-recognition pipeline on an image. |
| parse_documentC | Run the latest local PaddleOCR-VL pipeline on an image or PDF. |
| smart_ocrB | Route simple images to OCR and PDFs/large images to PaddleOCR-VL. |
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 3 tools
ocr_image and parse_document both accept images, and smart_ocr is a routing wrapper that overlaps both. The descriptions help somewhat, but an agent cannot clearly determine whether to call smart_ocr or the specific pipeline for a given input.
ocr_image and parse_document follow a verb_noun pattern, but smart_ocr is an adjective_noun exception. The naming is readable and consistently lowercased with underscores, yet the verb styles are mixed.
Three tools is a reasonable size for an OCR-focused server. The count is not excessive, though smart_ocr makes the set feel slightly redundant since it wraps the other two tools.
The set covers basic image OCR, document-level parsing, and PDF handling, which matches the apparent domain. Minor gaps exist around configuration options like language selection or output formatting, but core workflows are supported.