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

With no annotations, the description carries the full burden and fully satisfies it: it states default read-only behavior, warns about potential form submission and deletion when tests run, explains setup/config requirements, and clarifies auth handling (no secrets passed). It also discloses the return of setup instructions when uxlint.toml is missing.

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 long but well-structured with clear sections (WORKFLOW, SAFETY, SETUP, AUTH) and front-loaded with the core purpose. Some redundancy exists (e.g., verify_fix mentioned multiple times), but the detail is justified given the tool's complexity and absence of annotations.

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, lack of annotations, and no output schema, the description is exceptionally complete. It covers what it does, what findings return, sequenced workflow, safety and auth behaviors, setup requirements, and integration with other tools, leaving no critical gaps for an agent to understand and invoke 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?

The input schema already provides comprehensive descriptions for all 6 parameters (100% coverage), so the baseline is 3. The tool description adds only indirect context (e.g., workflow references base and crawl) without significantly expanding parameter semantics beyond the 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?

The description opens with a specific verb and resource ('Audit a website's UX/design') and enumerates concrete audit dimensions (contrast, tap targets, type scale, etc.). It clearly distinguishes itself from sibling tools like get_shot, ux_guidance, and verify_fix by focusing on the full audit and its findings.

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 WORKFLOW section explicitly prescribes when to use this tool and how to sequence it with ux_guidance and verify_fix. It also includes safety guidance (only point at owned apps) and notes when lint_feedback exists, covering both when and when not to use certain behaviors.

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 discloses that reports are PRIVATE, reads requires the user's uxlint login, returns image inline (if client supports MCP images), and always writes to a local file. This adds significant behavioral context beyond what an annotation might provide, such as side effects (writing a file) and authentication requirements. The only minor gap is not detailing the exact file path or cleanup behavior, but the description is quite 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 concise (about 3 sentences) and front-loads the core action. Every sentence provides essential information: purpose, why it's needed, what to pass, and what happens. No fluff or redundancy. The structure is logical: what, why, how, outcome.

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 that there is only one parameter, no output schema, and no annotations, the description is complete. It covers the tool's purpose, usage, parameter source, return behavior (inline and local file), and the limitation about private reports. This is sufficient for an agent to select and correctly invoke the 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% and it already explains the screenshot_url parameter clearly. The description adds contextual meaning by tying the parameter to the finding's screenshot_url and specifying the source (audit_url/verify_fix). It also clarifies that the URL can be a full URL or a /r/… path. This adds value beyond the schema, so a slightly above baseline score is warranted.

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: to view a report's annotated screenshot, with the flagged element boxed. It also distinguishes it from siblings by explaining why a plain GET won't work and that it requires the finding's screenshot_url. The verb 'View' and specific resource 'report's annotated screenshot' are precise.

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 explicitly explains when to use this tool (to fetch a screenshot_url from audit_url/verify_fix findings) and why it's necessary (reports are private, plain GET won't work). It also provides context that the screenshot URL comes from specific sources, serving as an alternative to direct fetching. This is exactly the kind of usage guidance expected.

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, the description carries the full burden for behavioral disclosure. It fully explains the tool is a read-only reference, details its fallback behavior for unrecognized topics, and notes it returns guidance content without side effects. No hidden be­havioral traits remain undisclosed.

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 long but well-structured; it opens with the main use case, specifies the covered patterns, and clarifies the auditing feedback loop. While it includes additional detail than strictly necessary, that extra context is valuable for guiding topic selection and instrumental in avoiding misunderstandings.

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 simplicity (one parameter, no output schema), the description is complete. It explains the purpose, usage flow, content scope, behavior with invalid input, and connection to sibling tool. No significant information is missing for an agent to invoke and interpret 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?

The schema description already fully explains the topic parameter (available values, aliases, fallback). The description adds illustrative examples (layout, forms, etc.) and mentions specific patterns but does not contribute new semantic meaning beyond what the schema already provides. Given the high schema description coverage (100%), a baseline score 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 provides best-practice UI guidance to read before building or changing UI, specifying the resource ('guidance') and the action ('read'). It distinguishes itself from sibling tools like audit_url and get_shot by focusing on pre-empting audit findings rather than auditing screenshots.

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 explicitly couples the guidance with a workflow: read the topic before building, then confirm with audit_url. This clearly contrasts with the sibling options for when to use this tool, providing practical 'when to use' and 'when not to use' context.

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 the full burden, and it delivers: it discloses that this is fast, deterministic, has no crawl and no judge, returns whether the rule still fires plus other deterministic findings, and warns that whole-site rules can pass here but still fail a full audit. The scope limitation ('clear verdict covers the ONE page it loads') is clearly stated.

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 longer than average but every sentence earns its place: purpose, return behavior, regression-guard semantics, alternative tool routing, and scope caveats. The most decision-relevant info is front-loaded, and the caveats are deliberately packaged at the end.

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?

Despite having no output schema and no annotations, the description is complete for an agent to select and invoke the tool correctly: it states what triggers usage, what is returned, what the tool does not do, and when to fall back to audit_url. No critical decision or invocation detail is missing.

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 fully documents base, rule, route, and states. The description reinforces the conceptual 'one rule on one page' model but adds no parameter-level meaning beyond the schema, so the 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 states a specific verb (re-check/verify), resource (ONE rule on ONE page), and the exact workflow context ('After editing to fix a finding'). It explicitly distinguishes itself from a full re-audit and from the sibling audit_url, making the tool's purpose unmistakable.

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 says exactly when to use it (the 'did my fix land?' loop after an edit) and when not to use it (for whole-page pictures, judge/state checks, or whole-site rules, re-run audit_url). This is explicit, actionable routing guidance that names the alternative tool and the condition that selects it.

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