Extract text from images and PDFs by automatically routing simple images to OCR and large documents to vision-language models for accurate results.
Route simple images to OCR and PDFs/large images to PaddleOCR-VL.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | ch | |
| image_path | Yes | ||
| force_model | No |
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | ||
| blocks | No | ||
| markdown | No | ||
| routed_to | Yes | ||
| source_name | Yes | ||
| elapsed_seconds | Yes |
Changes observed during successful MCP inspections.
v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of explaining behavior. It discloses the core routing behavior, but it does not explain what happens for unsupported inputs, how the 'simple' vs 'large' distinction is determined, or whether force_model overrides the routing logic. Some behavioral context is present but incomplete.
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 one short, front-loaded sentence with no redundant words. It earns its place, though the extreme brevity leaves room for important missing details.
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?
While the output schema exists, the description is insufficient for reliable invocation: it does not clarify parameter semantics, the threshold for 'simple' vs 'large', the role of force_model, or how this tool relates to the sibling tools. An agent would need to guess at key invocation details.
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?
Schema description coverage is 0%, and the description does not explain any of the three parameters. image_path, language, and force_model are all left entirely to the agent to infer from their bare names and the generic routing statement. This is a significant gap for a tool with three parameters.
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 uses a specific verb 'Route' and names two concrete OCR destinations, making it clear this tool selects between standard OCR and PaddleOCR-VL based on input characteristics. However, 'simple images' is vague and does not fully differentiate it from sibling tools like ocr_image or parse_document without more context.
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?
It gives clear usage context: use standard OCR for simple images, and PaddleOCR-VL for PDFs/large images. There is no explicit when-not-to-use guidance or mention of sibling alternatives, which keeps it slightly below a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.