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

No annotations are present, so the description carries the behavioral disclosure burden. It lists what the scan looks for and promises 'Findings with fixes,' which implies a non-mutating analysis that returns suggestions. However, it leaves ambiguity about whether fixes are applied automatically or merely proposed, and it does not mention output structure or limitations.

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 two compact sentences with no filler. Detection categories are front-loaded, and the output promise is stated in one crisp fragment, 'Findings with fixes.'

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 low-complexity tool with a single well-documented parameter and no output schema, the description provides the essential purpose and output promise. It would benefit from clarifying whether fixes are returned as suggestions or applied to the input, but the invocation context is otherwise adequate.

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?

There is a single 'text' parameter with 100% schema description coverage ('Article prose, plain text'). The description repeats the prose idea but adds no new parameter-level detail such as length limits, accepted formats, or normalization behavior, so it stays at the baseline for high schema coverage.

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 opens with a specific verb and resource: 'Scan prose for machine-writing tells.' It enumerates concrete detection categories (stock phrases, em/en-dash density, arrow glyphs, emoji), making it readily distinguishable from siblings like readability_check or seo_onpage_check.

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 intended use is implied by the description: use when you need to detect machine-writing tells in prose. However, it never explicitly states when to prefer this tool over qa_full_report, readability_check, or seo_onpage_check, nor does it give any when-not-to-use 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 the burden. It discloses that the SEO check only runs on HTML input, which is a behavioral constraint. However, it doesn't mention whether the tool is read-only or if it has any side effects, rate limits, or other behavioral traits. The description is honest but not exhaustive.

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 two sentences, front-loaded with the core purpose and the key constraint about HTML. Every sentence earns its place, and there is 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?

Given the tool's complexity (running multiple checks) and the absence of an output schema, the description does a good job of setting expectations: it returns a single report with fixes attached. It could mention the format of the report or whether it's a summary, but the description is adequate for an agent to decide to call it.

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 schema already documents both parameters. The description adds context that 'input' can be raw HTML or plain text and that HTML is preferred, which is useful. It doesn't add much about 'focusKeyword' beyond what the schema says, but the schema already covers it. 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?

The description clearly states the tool runs every check (readability, AI-writing tells, on-page SEO) on one article and returns a single report. It distinguishes itself from the sibling tools by being the comprehensive 'full report' option, while the siblings are individual checks.

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 when to use this tool: when you want all checks at once. It also provides a clear preference for HTML input and notes that the SEO check only runs on HTML. However, it doesn't explicitly state when to use the individual sibling tools instead, though the distinction is fairly obvious from the name and description.

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. 'Plain text in; findings with fixes out' clearly signals a side-effect-free analysis operation returning suggestions, and enumerates the three specific checks performed. It doesn't disclose edge cases (e.g., handling of non-prose input) but for a stateless analysis tool the core behavioral profile is well covered.

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 compact sentences with zero waste. The analysis types are front-loaded in the first sentence, and the input/output contract is packed into a pithy second sentence. Every word earns its place.

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 single-parameter analysis tool with 100% schema coverage and no output schema, the description is nearly complete: it states input format, the three checks, and the output shape (findings with fixes). The only minor ambiguity is what 'fixes' precisely means — whether suggestions or corrected text — but the core calling contract is clear.

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% — the single 'text' parameter is already documented as 'Article prose, plain text'. The description's 'Plain text in' is mostly redundant with the schema. Baseline 3 applies here since the schema fully handles parameter documentation and the description does not add meaningful syntax or format detail beyond it.

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 names a specific resource ('article prose') and a clear operation with concrete analysis dimensions: Flesch-Kincaid grade, long-sentence and passive-voice findings. This distinctly separates it from its siblings (qa_full_report, ai_tell_scan, seo_onpage_check), which cover different QA, AI and SEO concerns respectively. An agent can immediately tell what this tool produces.

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 purpose is clear enough that an agent can infer when to use it (readability analysis of prose), but the description gives no explicit when-to-use/when-not-to-use guidance and names no alternatives. Siblings cover different domains so confusion is unlikely, but no exclusions or selection criteria are stated.

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 implies a read-only analysis but never states that it makes no modifications, requires no authentication, or has side effects. The input constraint is mentioned, but behavioral expectations (e.g., safety, external calls) are absent.

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, zero fluff. The core list of checks is front-loaded, followed by constraints. Information density is high with no redundant wording.

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?

There is no output schema and no mention of what the tool returns (score, report, list?). Since the tool has only two simple parameters, the description covers input well, but the absence of any statement about the return value leaves agents guessing. An agent cannot reliably use the output without more info.

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% (both html and focusKeyword have descriptions). The description adds minor context by enumerating SEO aspects and the 'one article' scope, but it doesn't go beyond what's already obvious from the parameter names and schema.

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 lists the specific checks (title, meta description, heading structure, etc.) performed on a single article's HTML. The verb 'check' is implied but the resource is explicit. It doesn't distinguish itself from siblings like qa_full_report, but the purpose is unambiguous.

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 phrase 'Built for well-formed CMS article output (WordPress, Ghost, static sites), not adversarial markup' provides clear context on when to use (clean CMS HTML) and explicitly rules out one exclusion. However, it doesn't compare to siblings like readability_check or qa_full_report, so it earns a 4 rather than 5.

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