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

With no annotations, the description carries the behavioral burden and does disclose two non-obvious traits: helpers cannot see the repo (context isolation) and the caller remains responsible for verifying output (quality caveat). It does not cover failure modes, latency, or the parallel-call implications of model='all', which the schema only partly addresses.

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, front-loaded with purpose and cost rationale, then the context requirement and the verification caveat. Every sentence carries actionable information; the example list is compact and aids routing rather than padding.

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 4-parameter tool with a fully documented schema and no output schema, the description covers purpose, context requirements, and the verification obligation well. It is slightly thin on what the caller receives back (a text answer) and on behavior when model='all' fans out to multiple helpers.

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 reinforces that `prompt` must be complete and self-contained and that `system` carries optional extra instructions, but it adds no syntax or format detail 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?

States a specific verb (delegate) and resource (a self-contained chore to a cheaper helper model), plus the concrete payoff (save Claude tokens). The examples of delegated work and the contrast with sibling tools like web_search/web_fetch/generate_images make the boundary 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?

Gives clear when-to-use context through enumerated task types (summarising docs/logs, drafting boilerplate, translating, brainstorming, explaining an API) and warns to supply full context. It stops short of naming an explicit alternative or a when-not-to-use condition, so it is clear context rather than full routing 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?

With no annotations, the description carries the full burden and does well: it discloses parallel multi-model execution, on-disk side effects (files written to out_dir), that results are returned for human/model selection, and relative cost of the judge mode. It omits failure/partial-failure behavior and permission or rate-limit considerations, keeping it out of the top band.

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 purpose and workflow are front-loaded in the first sentence, and the inline enumeration of review modes is dense but earns its space. The quoted mode syntax is slightly awkward to parse but no sentence is redundant.

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 10-parameter tool with no annotations and no output schema, the description covers the essential lifecycle: generation, persistence, return-for-review, and the manual copy step. Remaining gaps (what the return payload looks like per mode, error handling when a model fails) are modest but real.

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 80%, so the baseline is 3, but the description adds real value by explaining the three 'review' enum values (paths/judge/inline) and their implications, which the schema does not document. It also clarifies the 'models' default (all of OAC_IMAGE_MODELS) and the out_dir default. The size/quality/background knobs are left to the schema, which is acceptable given the high 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 states a specific verb and resource ('Generate images') plus the distinctive mechanism: it fans out across every configured model in parallel, saves candidates to disk, and returns them for selection. This is far more informative than a tautological restatement and lets an agent understand the orchestration behavior immediately.

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 gives explicit mode-selection guidance ('paths' default, 'judge' is cheapest, 'inline' embeds images) and a clear post-step ('Copy the winner into the project yourself afterwards'). It stops short of stating when to prefer this tool over the unrelated siblings (web_search/web_fetch), but for a specialized generator that omission is minor.

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 supplied, so the description carries the full behavioral burden. "Show" implies a non-destructive read, which is appropriate, and it distinguishes configured ids from backend-advertised ones. However it never confirms read-only behavior or states whether any auth or environment context is required.

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?

A single sentence with no filler, and the key distinction (configured vs advertised) lands up front. Slightly dense with jargon ("helper model ids", "role") but nothing is wasted.

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 zero-parameter introspection tool with no output schema, the description adequately conveys what is reported: per-role configured ids and backend-advertised ids. Given the trivial input surface, the only real omission is when to use 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?

The tool takes zero parameters, so the baseline of 4 applies. There is nothing for the description to clarify about inputs.

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 clear verb ("show") and resource ("helper model ids"), and adds scope detail: ids configured per role plus ids the backend advertises. That distinction is informative beyond a restatement of the name. It does not need sibling differentiation since ask_agent, web_search, web_fetch, and generate_images are unrelated operations.

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 guidance on when to call this tool, what scenario prompts it (e.g., debugging model routing or verifying configuration), or any prerequisites. The description only says what is shown, never when an agent should reach for it.

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 and does disclose meaningful behavior: a helper model processes the page and returns only the answer when `question` is set, and otherwise cleaned (and capped) page text is returned. It omits auth requirements, rate limits, and failure modes such as dynamic or blocked pages.

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, front-loaded with the core action, then the two modes, then the cost rationale. No filler; every sentence 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?

With no output schema and no annotations, the description does the work of explaining what each mode returns, which is the key unknown for a fetch tool. It stops short of covering error/dynamic-content behavior and what the text cap actually is, but the essentials for calling it correctly are present.

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%, so the baseline is 3, but the description goes further by explaining the mechanical consequence of supplying `question`: a downstream model reads the full page and returns just the answer, versus raw cleaned text without it. This adds real meaning beyond the schema's brief descriptions.

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 ('Read a web page'), and the two operating modes are spelled out. It does not explicitly name or contrast with the sibling web_search, so an agent still has to infer the fetch-vs-search distinction, but the core 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?

It gives clear mode-selection guidance for the `question` parameter ('far cheaper than loading the page into Claude'), which is effectively a cost-based routing hint. However, it never states when to choose this tool over siblings like web_search or ask_agent, so tool-level usage guidance is only implied.

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, and it does meaningfully more than the name: it discloses the return contract (grounded answer + source URLs, not pages), a token-saving rationale, and a real edge case ('NOT A LIVE SEARCH' prefix means possibly stale). It omits rate limits, freshness guarantees, and auth needs, which keeps it out of 5 territory.

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?

Four short sentences, front-loaded with the purpose and output contract, then usage triggers, then the staleness caveat. Nothing is redundant, though 'done by helpers' is slightly vague and could be tightened.

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 annotations and no output schema, the description is the sole source of behavioral context, and it covers purpose, return shape, usage triggers, and a notable result-prefix caveat. It leaves out pagination/limits behavior and freshness details, but is sufficient for correct invocation.

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 67%, with query and focus already documented in the schema and the query guidance ('be specific — versions, dates, error text') largely duplicated in the description. The description says nothing about 'focus' or the 'max_results' bound (1-10, default 5), so it adds little beyond the structured fields; 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?

States a specific verb+resource ('Live web search') and immediately defines the output shape ('short grounded answer + source URLs instead of raw pages'), which implicitly separates it from the sibling web_fetch that returns raw pages. An agent can pick between web_search and web_fetch without opening either schema.

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 concrete when-to-use triggers ('docs, error messages, versions, news') and contrasts with the raw-page alternative. It stops short of explicit exclusions (e.g., when to prefer web_fetch or ask_agent instead), but the context is clear enough to route correctly.

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