Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It reveals that the operation is non-blocking and mentions the empty status, but does not explain what it returns on a success path (e.g., does it return a list of annotations or a status?), nor whether the fetch consumes the annotations. This is a significant gap since there is no output schema.

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 concise, two sentences, with the core purpose stated first. It fronts the key information (non-blocking fetch, fallback for wait_for_annotations, purpose) and includes an edge-case note. No fluff.

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?

The tool is simple (no parameters, no output schema), so the description needs to explain the return value. It only mentions the empty case (status='empty') and leaves the non-empty case unspecified. Without an output schema, this is a notable gap for an agent that needs to interpret the result. The purpose and context are clear, but return details are incomplete.

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 tool has zero parameters and the schema coverage is 100% (vacuous). Since there are no parameters, the description is not required to add any parameter semantics. Baseline of 4 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 identifies a clear verb (non-blocking fetch), a specific resource (recent committed annotations), and explicitly frames it as a fallback to wait_for_annotations, which distinguishes it from that sibling. However, the terms 'recent batch' and 'committed' are not defined, leaving some ambiguity about scope.

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?

The description provides a clear usage hint: it is for recovering context after an interrupted timeout chain. It also states the edge case of returning status='empty' when no annotations exist. It does not explicitly rule out other tools like open_svg or set_status, but the context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It discloses the browser-opening side effect, the requirement to await user annotations, and the behavior that source_script is echoed back with each annotation batch. However, it does not clarify whether the tool mutates the SVG file, what the immediate return value is, or what happens if the user cancels without annotating. The disclosed behaviors are useful but incomplete for a tool with zero annotation coverage.

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 compact and front-loaded, leading with the core purpose before elaborating on parameters. Each sentence adds distinct information: the workflow step, the path requirement, the source_script semantics, and the title behavior. Minor redundancy exists between the opening line and the operational details, but overall it is efficient with no filler.

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 a tool with no annotations and no output schema, the description is reasonably complete: it covers purpose, the required follow-up action, parameter semantics, and the round-trip behavior of source_script. Gaps remain around the return value format and error/cancel handling, but these are not critical for an agent determining whether and how to invoke the tool correctly.

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%, so the description must fully compensate. It does: svg_path must be absolute (syntax constraint), source_script is contextualized as the generation script (e.g., matplotlib fig_*.py) with its return behavior explained, and title is described as appearing in the top bar. This adds real meaning beyond the bare property names and types for all three parameters, nearly closing the coverage gap.

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+resource ('打开一个 SVG 图') with clear intent (供用户圈选区域写批注). It clearly differentiates from sibling tools: wait_for_annotations and get_annotations are about retrieving annotation data, whereas this tool handles the opening/display action. The purpose is unambiguous and contains no tautology.

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?

Explicitly instructs the agent to call wait_for_annotations after opening, establishing the correct sequential workflow. It also clarifies the conditional choice between editing the SVG vs. re-running the source script via the source_script parameter. It lacks explicit when-not-to-use guidance but provides solid operational direction; the alternating tool routing is implied through sequential instructions rather than named exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

无任何注释，描述须独立承担行为披露责任。它说明了推送状态文字的行为，并解释了自动刷新机制导致无需再次调用，提供了基本行为上下文。但未说明消息的显示时长、是否覆盖旧消息、失败处理等细节，行为透明度有限。

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?

描述仅两句话：第一句明确动作并给出示例，第二句补充使用时机和自动刷新逻辑。内容紧凑、无冗余，关键信息前置，结构合理，是一份高效规范。

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?

工具简单（单参数，无输出模式），描述涵盖了用途、时机和副作用（自动刷新的自解释）。虽然没有详细的行为细节（如消息清除方式），但鉴于工具的简单性，描述已足够让代理正确调用。

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?

输入模式中message参数仅有标题“Message”，无描述，schema描述覆盖率为0%。描述通过示例「正在修改 fig_xxx.py 并重跑…」暗示了参数应包含的文本内容，这为代理提供了有用的语义补充，但未明确参数格式、长度限制或是否必须为字符串，补偿不完整。

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?

描述明确说明工具是“向批注页面顶栏推送一条状态文字”，并给出具体示例「正在修改 fig_xxx.py 并重跑…」。动词和资源明确，与兄弟工具（open_svg、wait_for_annotations、get_annotations）功能截然不同，区分度高。

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?

描述给出了明确的使用时机：“修改开始前调用一次”，以及何时不使用：“改完文件后页面会因文件变化自动刷新,无需再调用本工具通知”。这提供了清晰的when和when-not，但没有提及替代工具（尽管兄弟工具功能不同，不需要排除）。

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It meticulously explains the blocking behavior, the timeout status as non-error, the retry protocol, and the detailed structure of each returned annotation (kind, note, geometry_svg, hits, texts_in_region). It also clarifies that timeout is not an error and how to proceed. This is exceptionally transparent for a wait operation.

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 well-organized: it opens with the core purpose and immediate return, then clearly explains the timeout protocol, and finally lists the annotation fields. Every sentence serves a purpose, with no redundant phrasing. It is concise yet comprehensive, front-loading the most important behavioral information.

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?

The description is thorough for a wait tool: it covers the blocking behavior, timeout handling, retry instructions, and the full return payload structure. It does not describe error scenarios (e.g., if the session expires) or what happens if the user submits invalid annotations, but given that there is no output schema and the tool is relatively simple, the provided details are largely sufficient. A small gap prevents a perfect score.

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 schema has 0% description coverage for the single parameter timeout_s (default 120), and the description does not explicitly explain this parameter. However, the description mentions status='timeout' and the retry protocol, which implies that the tool waits for a specified period. This indirect inference is not as clear as explicitly stating 'timeout_s is the maximum wait time in seconds.' The description adds some context but doesn't fully compensate for the lack of schema detail, so a 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 clearly states the tool's purpose: '阻塞等待用户在浏览器里提交批注' (blocks waiting for user annotations) and that it returns the annotation content immediately upon submission. It identifies the specific verb 'wait' and the resource 'annotations', making it distinct from siblings like get_annotations (which likely retrieves existing annotations) and open_svg. However, it doesn't explicitly name a sibling alternative, so it stops short of a 5.

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?

The description provides explicit instructions on when to use this tool: it should be called when waiting for user annotations, and if status='timeout', it should be called again to continue waiting. It also defines a clear stopping condition: until status='submitted' or the user explicitly declines to annotate. It doesn't discuss when not to use it relative to siblings, but the protocol is sufficient for correct usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.