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

With no annotations, the description must carry the full burden. It explicitly states the tool is for reading public docs (implying read-only, no auth), and it discloses the two possible return behaviors. It doesn't detail error cases or edge limits, but for a simple public-read tool this is reasonably transparent.

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 a single, well-structured sentence that front-loads the core purpose and then clarifies the conditional behavior. Every word earns its place with no redundancy or 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 one optional parameter and no output schema, the description is fully sufficient. It explains both invocation modes and their return types. There are no hidden behaviors or missing context that would confuse an agent.

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 already describes the slug parameter fully (including that omitting it returns the index). The tool description restates the same behavioral condition, adding no new meaning beyond the schema. Since schema coverage is 100%, the baseline of 3 applies.

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 reads public help documentation for '小分身' (specific verb+resource). It distinguishes two modes based on the optional slug parameter, which is specific and differentiates it from sibling tools like read_public_share and list_image_prompt_templates.

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 clear conditional context: omitting slug returns the /llms.txt index, while providing slug returns the corresponding Markdown. It does not explicitly mention alternatives or exclusions, but the scope is well-defined enough for an agent to infer when to use this tool.

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 burden. It explicitly states '系统级只读' (system-level read-only) and '不会触发生图' (does not trigger image generation), which are valuable behavioral guarantees beyond the tool's name.

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 short sentences, each providing unique information: scope, content, filter option, and side-effect guarantee. Zero 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 simple read-only list tool with one optional parameter, the description covers purpose, content, filtering, and safety. No output schema is needed because the description already mentions what is returned (prompt and preview image).

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 already describes slug at 100% coverage, but the description adds a more user-friendly phrasing '可选 slug 过滤单条', reinforcing its optionality and filtering behavior, which is somewhat redundant but still helps.

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 '列出' (list) with a clear resource '生图提示词模板' (image prompt templates), and distinguishes it from siblings by focusing on template listing rather than reading shares or product docs.

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 mentions the optional slug filter for narrowing to a single template, which gives context for when to pass parameters. It doesn't explicitly contrast with sibling tools, but the purpose is distinct enough to imply usage.

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 burden. It explicitly discloses what the tool returns (title, body, cover, signature) and what it excludes (conversation context). It also uses 'snapshot' to imply a fixed view. While lacking details on auth or rate limits, it covers key behavioral aspects well.

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?

Two sentences, front-loaded with purpose, then usage, then output. Every sentence adds value with no redundancy or 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 simple read tool with one parameter and no output schema, the description covers purpose, input, and return values, including an explicit exclusion. It misses example usage or edge-case behavior, but is sufficiently complete for the tool's complexity.

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?

With one parameter and 100% schema description coverage (the schema already describes 'URL or token'), the description adds no additional meaning beyond repeating the same information. Baseline 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?

Description uses a specific verb ('读取' / read) and clearly identifies the resource ('用户主动公开的小分身文章快照'). It also distinguishes from siblings by focusing on reading public shares, as opposed to listing templates or getting product docs.

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 clear context for when to use—specifically for publicly shared articles—and explains the accepted input (URL or token). However, it does not explicitly mention alternatives or when not to use, so it falls short of a full 5.

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