baidu_ocr_table
Extract structured table text from images. Send an image URL to receive recognized table content.
Instructions
[OCR] 表格文字识别 — $0.02/call (free: 5/5 today)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | 图片URL |
Extract structured table text from images. Send an image URL to receive recognized table content.
[OCR] 表格文字识别 — $0.02/call (free: 5/5 today)
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | 图片URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only mentions cost and free quota. It does not disclose behavioral traits such as input format expectations, output structure, or potential limitations (e.g., only works with certain table types).
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 very short and front-loaded with type and cost. While it could include more useful detail, it wastes no words and is efficient.
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?
Given many sibling OCR tools and no output schema, the description is incomplete. It fails to explain what differentiates table OCR or what the agent should expect as output, limiting usability.
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 coverage is 100% (one parameter 'image' with description '图片URL'). The description adds no extra meaning beyond the schema, so a baseline of 3 is appropriate.
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 states '表格文字识别' (table text recognition) with an '[OCR]' prefix, clearly indicating the tool's function of recognizing text in table images. It distinguishes itself from general OCR siblings like baidu_ocr, but could be more explicit in English.
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?
No guidance on when to use this tool versus other OCR tools for tables, like baidu_ocr or baidu_ocr_accurate. The lack of context for selection among many siblings makes it difficult for an agent to choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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