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

With no annotations provided, the description carries the full burden and delivers: it discloses the sampling behavior ('Fetches a sample of the advertiser's creatives'), the cost model ('Roughly one to two requests per creative'), and the non-opinionated nature ('Pure aggregation — read strategy into it yourself'). It does not cover error handling for unknown advertisers, but the core behaviors are 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?

Front-loaded one-line summary, then a detailed aggregation list, then an Args/Returns block. Every sentence adds information, and the Args documentation is justified because the schema has no parameter descriptions. Slightly long, but well-organized with critical scope info first.

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 2-parameter tool with no annotations and no output schema, the description compensates well by including an explicit Returns section listing the full result shape (status, advertiser_id, sampled, by_format, etc.). The only gap is failure behavior for unknown advertiser ids, which is minor for a read-only summary 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 0%, so the description fully compensates: it documents advertiser's three accepted formats (id 'AR...', domain, name) and sample's default (50), maximum (100), and per-creative request cost. This is exactly the semantic information the input schema omits.

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 ('Summarise') and resource ('one advertiser') and enumerates the three aggregation outputs (format mix, copy sample, destinations). The detailed list of what is aggregated (headlines, descriptions, CTA labels, destination hosts) distinguishes it from siblings like compare_advertisers (multiple advertisers), get_ads/get_ad_detail (single ad retrieval), and search_advertisers (finding advertisers).

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 scope 'Summarise one advertiser' and the disclaimer 'Pure aggregation — read strategy into it yourself' imply the tool is for read-style analysis of a single advertiser rather than decision-making. However, it never explicitly names a sibling alternative or states when not to use it (e.g., versus compare_advertisers for comparing multiple advertisers); the usage context is implied, not stated.

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 present, the description shoulders the full burden and does it thoroughly. It explains exact status semantics, especially the subtle trap that "blocked" is a Google rate-limit and is NOT a zero, while "absent" IS a genuine zero. It also discloses row ordering and parent_entity caveats, materially helping an agent interpret results correctly.

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?

Every section earns its place: a one-line core purpose, a short positioning sentence, then a compact but vital status legend, and shorthand input/output contracts. The front-loaded purpose lets an agent quickly identify the tool, and deeper detail is deferred until after the first paragraph.

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 fully covers the only input param, the non-obvious status codes, the specific meaning of parent_entity rows, and the ordering guarantees. Since an output schema exists, the Returns listing is a convenience, and the tool description still appears complete for an agent 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?

Since schema description coverage is 0%, the description must compensate for the single `domains` parameter, and it does: it imposes the "Up to 50" limit and states that either domains or URLs are accepted, with an example. It leaves minor edge cases unstated (behavior with more than 50 items or duplicates), but covers enough for normal use.

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-plus-resource statement: "Compare active Google ad counts across a set of domains, one row each." It separates this tool from the sibling search/get/creative tools by emphasizing the cross-domain, batch comparison shape and the single-call competitive question it answers.

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 clearly frames when to use it: "The competitor paid-surface question in one call: who is running ads, and how many." It also gives a meaningful exclusion—"never cite those counts as the domain's own" for parent_entity rows—but it never explicitly names sibling tools or says when to prefer them (e.g., for a single domain or for ad-level detail).

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 discloses meaningful behavior: files are written to out_dir, the directory is created if missing, each creative gets its own subfolder, videos are sampled to stills, and a manifest is returned. It doesn't address overwrite behavior or authentication/permissions, but it is substantially transparent for a download operation.

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 well-structured with an introductory purpose paragraph followed by Args and Returns sections. Every sentence adds value: the bulk-path context, the presentation use case, parameter details, and the return shape. No filler or redundant restatement of the tool name.

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 tool with no output schema and no annotations, the description is remarkably complete: it covers the operation's side effects, each input, and the return structure. The only notable gap is unspecified behavior when out_dir already contains files or the manifest already exists, which could matter for repeated calls.

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%, but the description compensates fully by explaining every parameter: advertiser_name with examples, out_dir behavior, count with default and max, full_quality meaning, and frames default with its purpose. This is exactly what an agent needs beyond 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 description names a specific action ('Download'), a precise resource ('a whole advertiser's creatives'), and an output shape ('into one folder, with a manifest'). It also positions itself as 'the bulk path', distinguishing it from the single-creative sibling fetch_ad_creative without needing to open that tool's 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?

It gives clear context for when this tool is appropriate: collecting real ad examples for decks or reports, in bulk rather than one at a time. It doesn't name alternative tools explicitly or state when not to use it, but the 'bulk path' framing makes the intended use unambiguous.

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 of behavioral disclosure. It explains that image ads are saved at original resolution, video ads are sampled into JPEG frames, the frames/width/full_quality parameters affect output, and text ads return decoded copy instead of a file. This is thorough and lets an agent predict the tool's behavior accurately.

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 well-structured: a concise opening, then an Args section listing each parameter with explanations, and a Returns section. It is front-loaded with the core purpose, and every sentence adds value—no filler. The length is justified given the parameter complexity.

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 output schema, the description includes a Returns section summarizing the return format and noting the text-ad special case. All six parameters are fully explained, including defaults and interactions. The tool's complexity is high (multiple formats, sampling options), and the description covers what an agent needs to call it correctly without missing critical information.

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 has 0% description coverage, so the description must compensate. It does so thoroughly: the Args section explains each parameter (advertiser_id, creative_id, out_dir, frames, width, full_quality) with usage details, defaults, and edge cases (e.g., width ignored when full_quality is set). This adds substantial meaning beyond the raw 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 precise verb and resource: 'Download an ad's actual creative to local files.' It clearly states what the tool does and distinguishes image vs video handling, making its purpose unambiguous. It also implicitly differentiates from siblings like get_ads or get_ad_detail by focusing on downloading the creative asset 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?

The description provides clear guidance on when to use specific options: 'Use for presentation; leave off for vision analysis' (referring to full_quality). It explains the difference between image and video handling and when to use full_quality vs not. However, it does not explicitly compare with sibling tools like download_ad_creatives, so it lacks an explicit 'when not to use' or alternative selection.

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, this description carries the full behavioral burden, but it only reveals the verb 'Get' and the return structure. It does not mention side effects, auth requirements, error behavior, rate limits, or any other operational traits, which is a notable gap for a no-annotation tool.

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 cleanly structured with a one-line summary followed by Args and Returns sections. It efficiently uses bullet-like formatting and avoids filler, though the Args/Returns sections could be considered slightly more verbose than strictly needed.

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 2-parameter tool with no output schema, the description provides a fairly complete picture: it lists all parameters, explains their purpose, and describes the varying return shapes by ad type. It misses only the operational edge cases (errors, permission requirements), which keeps it from full completeness.

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%, and the description effectively compensates by explaining both parameters with concrete examples. It identifies advertiser_id as the advertiser's ID and creative_id as the creative/ad ID, including readable example values, making it easy for an agent to fill them correctly.

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 starts with 'Get full details for a specific ad creative,' which names a clear verb and resource. The return details (format, last_shown, content type) add specificity, but it does not explicitly differentiate from siblings like fetch_ad_creative or get_ads.

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 implied use case is clear: you need this when you have both advertiser_id and creative_id and want detailed info on a single creative. However, there is no explicit distinction from siblings or any guidance on when not to use it, leaving the agent to infer without direct routing.

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?

The description discloses key behaviors: it searches by name, decodes text content, returns a list with specific fields (format, date, content). No annotations were provided, so the description carries the burden and does so adequately, though it lacks details on error handling or authentication.

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, front-loaded with purpose, and structured with Args and Returns sections. Every sentence adds value without redundancy.

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 only two simple parameters and an output schema present, the description fully covers what the tool does, its inputs, and output structure. It is complete for its complexity.

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?

With schema description coverage at 0%, the description fully compensates by explaining advertiser_name (with examples) and count (default, max). This adds significant meaning 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 clearly states it gets ads for an advertiser by name, with decoded content. It distinguishes from sibling tools like get_ad_detail (single ad) and search_advertiser_by_domain (domain-based 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 explains how the tool works (searches by name, fetches creatives, decodes content) and when to use it. However, it does not explicitly state when not to use it or alternative tools, though the sibling context implies differentiation.

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?

Despite no annotations, the description discloses return values (advertiser info with fields and null for not found) and implies a read-only operation through the verb 'search'. It could be more transparent about potential errors or edge cases, but it covers the main behavioral aspects.

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 a concise docstring with clearly separated Args and Returns sections. Every sentence adds value, and there is no redundancy or fluff.

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 simple single-parameter search tool with an output schema, the description covers purpose, parameter format, and return value adequately. No additional context is necessary for an AI agent 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 schema has 0% coverage for the 'domain' parameter, but the description adds significant meaning by specifying it is a website domain and providing examples ('nike.com', 'coinbase.com'). This fully compensates for the lack of schema description.

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 'Find an advertiser by their website domain,' which specifies the verb, resource, and search criterion. It distinguishes itself from siblings like 'search_advertisers' by focusing on domain-based lookup.

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 a clear context for when to use the tool (when you have a domain) and includes an example. However, it does not explicitly mention alternatives or when not to use it, though the purpose is sufficiently self-explanatory.

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, and the description only mentions the return fields (id, name, region, ad_count). It lacks details on behavioral traits such as pagination, result limits, ordering, or any side effects.

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 well-structured with Args/Returns sections and is clear, though the 'Args:' line is slightly redundant since there is only one parameter. It is efficient overall.

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 has an output schema (context signal: true), the description provides the return fields sufficiently. For a simple one-parameter search tool, it is nearly complete, but lacks behavioral details like pagination.

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 0% for the only parameter 'query', but the description adds concrete examples ('Nike', 'Coinbase', 'crypto exchange'), giving clear semantic meaning beyond the raw 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 clearly states it searches for advertisers by keyword or company name, with a specific verb ('Search') and resource ('advertisers'). It distinguishes from siblings like 'search_advertiser_by_domain' which is a different search variant.

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 by providing example queries (e.g., 'Nike'), but does not explicitly state when to use this vs alternatives, nor does it mention when not to use it.

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