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

With no annotations, the description is the sole source. It discloses the six analysers but does not mention whether the tool modifies the corpus, requires special permissions, or produces output side effects. This is a moderate gap.

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 action, no unnecessary words. Every sentence adds value.

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 covers the tool's purpose and lists the analysers, which is sufficient given the simple input schema and no nested objects. However, lacking an output schema, it could mention whether results are stored or returned.

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% and descriptions are clear. The tool description does not add extra meaning beyond the schema for the two parameters (corpus_name and corpus_dir). 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 clearly states 'Perform linguistic analysis on collected corpus' and lists the six specific analysers (phrases, voice markers, etc.), which distinguishes it from sibling tools like collect_corpus and generate_voice_skill.

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 implies use after corpus collection ('on collected corpus'), and the sibling context clarifies the analysis role. However, it does not explicitly state when not to use this tool or provide alternative guidance.

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 full burden. It does not disclose behavioral traits like whether crawling respects robots.txt, rate limits, authentication needs, or what 'clean' means. Users cannot infer side effects or constraints.

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 sentence that is front-loaded with the core action. It is concise, though it could be slightly expanded for clarity without sacrificing conciseness.

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 the tool has 5 parameters, no output schema, and no annotations, the description is too brief. It does not explain return values, error handling, processing details, or corpus structure, leaving significant gaps for an AI 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?

Schema description coverage is 100%, so the baseline is 3. The description adds a high-level purpose but no additional detail beyond what the schema already provides for each parameter. It does not compensate for missing nuance.

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 specific verbs ('crawl sitemap', 'collect clean writing corpus') and clearly identifies the resource and action. It distinguishes from sibling tools: 'analyze_corpus' suggests analysis, 'generate_voice_skill' suggests voice generation, so this tool's unique role of data collection is clear.

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 no guidance on when to use this tool versus alternatives (e.g., analyze_corpus or generate_voice_skill). No context on prerequisites, limitations, or when not to use it is given.

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 present, so the description carries full burden for behavioral disclosure. It mentions the output components and the technique of mimicking writing, but fails to disclose side effects (e.g., file creation), required dependencies, error conditions, or any limitations. The behavioral picture is 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 a single, front-loaded sentence that conveys the core purpose efficiently. No redundant phrases. While it could be slightly more structured for scanning, it earns its place effectively.

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 the absence of output schema and annotations, the description is minimally adequate. It explains what the tool produces and the core idea, but lacks details on prerequisites, error handling, output format, or post-conditions. The schema covers parameter semantics, but overall context is thin.

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 100%, so each parameter has a description. The tool description does not add additional meaning beyond the schema. According to guidelines, baseline is 3 when schema coverage is high, and no extra value is provided from the description.

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 clearly states the tool generates a Claude Skill bundle, specifies the output (SKILL.md plus article samples), and distinguishes its method from rule-based approaches. It contrasts with sibling tools (analyze_corpus, collect_corpus) by focusing on generation.

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 explicit when-to-use or when-not-to-use guidance. The description implies this is the final step after corpus collection/analysis, but does not state exclusions or alternatives. Sibling names provide some context, but the description lacks direct usage advice.

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