SEOWrench

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful return-shape context: two scores, severity-ordered problems, stable codes, measured facts, and fix guidance, though it does not mention auth or rate limits.

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 tightly structured and front-loaded: purpose first, then return format, then usage routing. Every sentence contributes information without repetition.

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 read-only audit tool with no output schema, the description explains what is returned well enough to set expectations: scores, severity ordering, problem codes, measurements, and fixes. The schema covers the two inputs fully.

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 url and lang parameters are already documented in the schema. The description adds only the public-page constraint and no additional parameter syntax, leaving the schema to carry parameter semantics.

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: auditing one public web page for technical SEO and AI readiness. It also distinguishes this from the sibling verify tool by explaining the before-editing versus after-editing relationship.

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 explicitly says to use this tool before editing a page and to use verify after editing, giving a clear condition and a named alternative. The usage boundary is directly actionable.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description goes beyond them by enumerating what the result contains — readiness score, robots.txt-blocked crawlers, llms.txt presence, and AI-specific problems with fixes — which is valuable since there is no output schema. It says nothing about latency, auth, or rate limits.

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 filler: the first establishes the operation and audience, the second enumerates the return payload. The most important clause (what is checked) is front-loaded.

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?

With no output schema, the description correctly carries the return-value burden and does so thoroughly, listing the score, blocked crawlers, llms.txt status, and fixable problems. It is slightly incomplete in not clarifying how it differs from audit_url/verify or when lang matters.

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 both the url and lang parameters are already documented in the schema. The description adds no format or constraint detail beyond what the schema provides, so the baseline 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?

States a specific verb and resource ('Check whether AI assistants ... can read and cite a page') and names the concrete platforms checked, so the agent knows exactly what operation this performs. It does not, however, distinguish itself from the sibling tools audit_url and verify, leaving overlap ambiguity unresolved.

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?

There is no explicit when-to-use statement, no prerequisites, and no routing advice relative to audit_url or verify. Usage is only implied by the word 'page' and the AI-readiness framing; an agent has to guess which of the three check tools to pick.

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?

Annotations only cover readOnly/openWorld; the description adds the genuinely non-obvious trait that the page is re-read live and 'never a cached result', plus the tri-state result vocabulary (passed / failing / not_measured). That is exactly the kind of behavioral detail annotations cannot convey.

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 tight sentences: purpose first, then the freshness guarantee, then the usage precondition. No filler, and the most decision-relevant facts are front-loaded.

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?

With no output schema, the description carries the return-value burden and does state the per-code verdicts. The meaning of 'not_measured' is left implicit, a minor gap for an otherwise simple three-parameter read tool.

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 url, lang and codes are already documented in the schema; the description only reinforces the origin of the codes. Baseline 3 applies since the description adds no format or syntax detail beyond the structured fields.

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?

States a specific verb and outcome ('Confirm that a fix worked') and immediately distinguishes itself from siblings by naming audit_url and geo_check as the sources of the codes it consumes. An agent can tell this is the post-change verification step, not the audit step itself.

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?

Gives explicit sequencing ('after you change the page') and routes the agent to the two sibling tools that produce valid codes. There is no explicit when-not-to-use statement (e.g. don't call before editing), so it stops just short of the top band.

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