Render a specified page as an image to visually extract text and structure from scanned documents lacking a text layer, enabling subsequent registration of facts and evidence.
将指定页渲染为图片返回（用于扫描件无文本层时的视觉识别）。模型应直接‘看图’提取文字与结构信息，再通过对应 record_* 工具登记。
参数结构（args 字段）: {volume(必填): 卷宗名称; page(必填): 页码（1-based）}
| Name | Required | Description | Default |
|---|---|---|---|
| args | No | ||
| case_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.3Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool renders a page to an image and is intended for visual extraction, but it does not describe response format, size limits, or whether any persistent state changes. The read-only nature is implied rather than explicit.
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?
Three compact sentences: one for purpose, one for downstream workflow, and one for parameter structure. It is front-loaded and contains minimal fluff, though the parameter block could be more tightly integrated with the schema.
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?
For its intended visual-recognition purpose, the description covers the core information: what the tool does, when to use it, and how to follow up. It falls short by not mentioning case_id, not explicitly contrasting with read_pages, and leaving the exact output image format to the output schema rather than giving any guidance here.
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?
The description adds meaningful parameter semantics by defining volume and page with required flags and specifying page is 1-based, which the input schema does not document. However, it omits case_id, the only top-level required parameter in the schema, and the schema's args object is unconstrained, creating a mismatch that could cause incorrect invocation.
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 a specific verb and resource: render a specified page as an image and return it for visual recognition when scanned documents lack a text layer. It clearly distinguishes its purpose from text-based reading tools by emphasizing the 'see the image' workflow and subsequent record_* registration.
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 an explicit use case: scanned documents without a text layer, and instructs the model to visually extract information then register it via record_* tools. It does not name an alternative like read_pages or state when not to use this tool, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.