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

With no annotations, the description carries the behavioral disclosure burden. It does reveal key behavior: matching is literal rather than regex or semantic, and the response consists of compact context windows around matches. However, it does not mention case sensitivity, failure modes, page loading behavior, or limits, leaving notable gaps.

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 contains the core action, the matching mode, and the response shape with no redundant words. It is front-loaded and easy to parse.

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 adequate for a simple tool, and the output schema likely covers return values. But with no annotations and no parameter documentation, it lacks details about max_matches behavior, exact context window semantics, and when to prefer sibling tools. It is minimally sufficient but not fully complete.

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 0%, so the description must compensate. It clarifies that 'pattern' is a literal phrase and 'context_chars' relates to compact context windows, but it does not explain 'max_matches', 'url', defaults, or the exact relationship between parameters and output. This is only partial compensation for the missing schema 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?

The description states a specific verb and resource: finding a literal phrase in a page and returning compact context windows around matches. The word 'literal' helps distinguish it from the sibling 'search' tool, which implies broader or semantic search. This is a clear, specific purpose statement.

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 usage: use this tool when an exact literal phrase is needed within a page. However, it does not explicitly say when not to use it or mention alternatives like 'search' or 'search_images'. The usage guidance is present only by implication.

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 discloses several useful traits: public-only access, rejection of private/local addresses, safe-follow redirect mode, and a response byte cap. It could also mention error behavior or timeout handling, but the provided constraints are substantial.

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 tight sentences front-load the purpose, then add usage context and behavioral constraints. 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?

The tool is simple and the description covers URL type, output format, redirect behavior, and a cap. The main gap is max_chars semantics, which matters because there is no schema-level documentation and no annotations.

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 0%, so the description must compensate. It adds that URL must be public HTTP(S), but it never explains the max_chars parameter or how the response cap relates to it. An agent cannot confidently tune max_chars based on this text.

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 ('Fetch'), resource ('public HTTP(S) page'), and output ('cleaned readable text'). This distinguishes it from siblings like search and search_images: it retrieves page content rather than result snippets or images.

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?

Explicitly says 'Use after search when result snippets are insufficient,' giving a clear trigger condition and relationship to the primary sibling. It also states a when-not: private/local addresses are rejected.

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 behavioral burden and handles it well: it discloses the DuckDuckGo/Bing fallback order, the cn.bing.com behavior for Chinese markets, and the meaning of quality_label=poor. This gives agents useful execution expectations beyond what the schema could 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?

The description is longer than average, but the length is justified by the need to explain query construction and engine quirks. The opening behavior is front-loaded, and the Args section is clearly organized. A small amount of redundancy exists, but every major sentence adds practical value.

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 output schema exists and annotations are absent, the description covers the key operational context: engine fallback, locale behavior, quality_label handling, and parameter semantics. It lacks explicit when-to-use versus search_images/open/find guidance, but the other information is sufficient for an agent to call and interpret results 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?

Schema description coverage is 0%, so the description must compensate, and it does thoroughly. Each parameter is explained: query receives detailed formulation rules, count is bounded to 1–20, offset to 0–100, market is tied to locale, and safe_search enum values are named. This is far more helpful than the bare 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 first sentence clearly states the action and scope: 'Search the public web.' The description goes beyond the title by specifying engine behavior (DuckDuckGo first, Bing fallback) and the Chinese-market variant, which distinguishes this tool from image or document navigation siblings.

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 provides strong guidance on how to construct queries, including concrete examples and disambiguation advice (e.g., '复旦光华楼' is better than '光华楼'). It also warns when not to trust results via quality_label. It does not explicitly name alternative tools like search_images, so some sibling differentiation is left implicit.

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 ranking mechanism, the auto fallback threshold, what each provider does, and the exact result signals: 0-100 text score, domain hint, and explainable signals. It even tells the agent how to assess confidence and when verification is needed, which goes well beyond a minimal tool description.

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 purpose and behavior, and the Args section is logically organized. It is longer than typical descriptions, but that length is justified by the zero-coverage schema and the need to explain provider behavior and scoring. Minor redundancy exists because provider defaults and enum values are repeated from the schema, but the added context still 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?

Given the tool's provider-switching complexity, fallback threshold, scoring semantics, and four parameters, the description provides everything needed to select and invoke it correctly. It explains query formulation, ranking confidence, provider differences, and optional verification workflow. The output schema covers return structure, so the description does not need to detail the exact JSON response.

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 0%, so the description must fully compensate for the schema's lack of parameter documentation. It does: `query` has concrete examples and wording advice ('复旦光华楼' is better than '光华楼'), `count` is bounded 1-20, `market` is explained as locale-specific to Bing while Commons is language-neutral, and `provider` enumerates the options. This is excellent parameter documentation.

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: 'Search image indexes and rank results with pure text so vision is not required.' This clearly distinguishes the tool from the sibling `search`, `open`, and `find` by emphasizing image indexes and text-based ranking. The provider variants (bing_images, commons) further specify exactly what kind of image search this is.

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 usable routing guidance: `auto` is the default, it falls back to Commons below ~40 text score, and results below ~40 should be treated as unverified. It also recommends verifying with `find`/`open` before downloading, which indirectly differentiates this search tool from sibling file/URL tools. It lacks an explicit 'when not to use this tool' statement, but the behavioral and provider guidance is clear enough.

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