ocr-mcp
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 | {
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ocr_recognizeC | 识别图片中的文字内容。支持本地文件路径或 URL。 |
| ocr_tableC | 识别图片中的表格内容，返回结构化的表格数据。 |
| ocr_handwriteB | 识别图片中的手写文字内容。 |
| ocr_formulaA | 识别图片中的数学公式，返回 LaTeX 格式。 |
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
The tools are mostly clearly separated by content type: general text, tables, handwriting, and formulas. There is minor overlap because ocr_recognize could plausibly also handle handwritten text or tables, but the specialized tools make their intended use clear.
All tools share the consistent ocr_ prefix, which makes the server easy to scan. The suffixes mix verbs and nouns (recognize/handwrite vs table/formula), but the pattern remains predictable and readable.
Four tools is well-scoped for an OCR server: one general recognition tool plus three specialized modes. Each tool has a clear reason to exist without overwhelming the agent.
The surface covers the main OCR use cases: plain text, tables, handwriting, and formulas. Minor gaps such as batch processing, PDF input, or language options exist, but they are not essential for the apparent core purpose.