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

With no annotations, the description carries the full burden. It discloses that id uses an already-seen URL, summary mode de-noises mainText, and the output includes truncated and cached fields, which inform the agent about caching and truncation behavior. It does not cover failure modes or rate limits, but that is beyond typical selection criteria.

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 three tight sentences followed by an explicit output shape, with no filler. The core action is front-loaded, and every subsequent sentence adds information an agent needs for correct invocation. The output spec is lengthy but necessary since there is no output schema.

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 no annotations and no output schema, the description fully compensates: it explains purpose, parameter usage, behavioral nuances, and return value structure. The explicit output JSON gives agents a precise contract of what to expect, making the tool effectively self-contained. No critical selection or invocation information 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 coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by clarifying that id looks up the already-seen URL and that summary extractMode 'saves tokens', directly addressing cost implications. The output block also connects maxLength to truncation and cached to caching behavior, which the schema alone does not.

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 'Fetch a page and return its content as structured Markdown', a precise verb-resource-output statement that leaves no doubt about the tool's function. It also differentiates from siblings by explaining the id comes from search/research, positioning fetch_page as the retrieval step after search.

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 explains when to pass 'url' versus the 'id' returned by search/research, including that id looks up an already-seen URL. It also gives a clear choice criterion for extractMode: summary saves tokens, full returns the complete page. It stops short of naming explicit alternatives or exclusions, but the workflow context is clear.

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 does so thoroughly. It discloses the domain-diversity selection strategy, the anti-bot 403 resilience reasoning, and the exact output shape including per-page error objects.

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 front-loaded with the core purpose and use guidance, then adds a compact but necessary output schema. Every sentence earns its place, and the JSON block is justified because there is no separate output schema.

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 complex 11-parameter tool with no annotations and no output schema, the description is remarkably complete. It explains the composite workflow, selection behavior, error handling, and return structure, so an agent has enough context to invoke it 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 description coverage is 100%, so the schema already documents every parameter, including defaults, aliases, and precedence. The description itself adds no parameter-specific meaning beyond what the schema provides, matching the baseline for full 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 'One-call research: search + fetch the top pages and return an evidence brief,' which clearly identifies the operation, the resource, and the composite behavior. It also explicitly distinguishes itself from the sibling tools by saying to use it 'instead of chaining search/fetch_page yourself.'

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 gives clear usage context: use this tool instead of manually chaining search and fetch_page. It names the relevant alternatives, though it does not spell out exclusions such as 'when you only need a single page, use fetch_page.'

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 behavioral burden, and it does so well by disclosing the full structured output shape, including query rewriting, engine switching, caching, deduplication, and freshness handling. It does not mention rate limits or authentication, but for a read-only search tool the output detail provides substantial transparency.

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 is front-loaded and the output block is dense and useful rather than repetitive. The only length comes from the necessary output schema, which compensates for the missing output-schema field. It is reasonably concise and every part contributes to the tool's usage.

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 is complete enough for invoking a search tool: it specifies behavior, output structure, and a follow-up action using fetch_page. It lacks explicit error/edge-case behavior and does not mention how research relates to this tool, but those are not critical given the rich schema and detailed output specification.

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 parameters are already fully documented in the schema. The tool description adds no additional meaning about the parameters themselves; it focuses on output structure. Baseline 3 is appropriate here because the schema does the needed work.

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: 'Search the web and return structured results.' It is clearly distinct from fetch_page, and the output schema plus the instruction to use result IDs with fetch_page reinforces that this tool is for discovery rather than reading full pages. However, it does not explicitly differentiate from the research sibling tool, so it falls short of a 5.

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 gives clear context that this tool is for web search and explicitly instructs the agent to use fetch_page with a result ID to deep-read a page, which clarifies one key alternative. It does not provide explicit when-to-use versus research or list exclusions, but the workflow is evident enough for correct selection in most cases.

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