HasData Google Maps

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

No annotations are provided, so the description carries the full burden. It describes the return fields and pagination but does not mention rate limits, errors, or authentication. It implies read-only via the GET method in the title but not explicitly in the 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 concise, with a clear verb, resource, and use-case sentence. It avoids fluff and is well-structured.

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 there is no output schema, the description lists the return fields (rating, text, date, place name, place address, placeId, photos, owner responses) and mentions pagination. It does not cover error handling or limitations, but for a simple GET endpoint it is reasonably 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?

All five parameters are described in the schema, and the description adds extra details such as the valid range for 'num' (10-200) and the purpose of 'nextPageToken'. This goes beyond simply restating 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 the tool's function: listing Google Maps reviews authored by a specific contributor. It uses a specific verb ('Lists') and resource ('reviews authored by a specific Local Guide / contributor'), distinguishing it from the sibling tool for place reviews (hasdata_google_maps_reviews_getMapReviews).

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 explicit use cases ('Use for reviewer reputation checks, detecting fake/bot review patterns, local-guide activity analysis, and building review-author profiles for trust scoring'). It does not explicitly compare with sibling tools, but the contributor focus makes the intended scenario 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 present, the description carries the disclosure burden. It discloses pagination via nextPageToken, filtering by categoryId, and the per-photo return fields (image URL, thumbnail, upload date, uploader, photoId). It does not cover auth, rate limits, or error behavior, but the read-only fetch semantics are clear from 'Fetches.'

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 compact and front-loaded, with the core fetch behavior first and use cases last. The opening heading duplicates the tool title slightly, but the remaining sentences are information-dense and each 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 there is no output schema, the description compensates by listing return fields and pagination behavior. It covers the main parameters (dataId/placeId, categoryId, nextPageToken) and provides use cases; only the optional hl parameter and required-parameter rules are left to the schema, which documents them adequately.

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 by enumerating categoryId values ('all, latest, menu, by owner, videos, street view'), clarifying the dataId/placeId alternative, and tying nextPageToken to pagination. This goes beyond the 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 opens with a specific verb and resource: 'Fetches the photo gallery of a Google Maps place.' It clearly distinguishes this tool from sibling Google Maps tools such as reviews, posts, and place details by centering on photos. The scope (by dataId or placeId) 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?

The description gives concrete use cases: 'restaurant-menu extraction, venue/ambience visual audits, building rich place detail pages, and sourcing up-to-date imagery for POI listings.' This tells an agent when the tool is valuable, but it does not explicitly name alternatives or state when not to use it, so it stops short of 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 provided, the description carries the transparency burden. It clearly states that the operation fetches (read-only) and enumerates the returned data fields, while also noting optional domain/language localization. It does not mention rate limits, error behavior, or response format, but for a simple GET-by-ID endpoint the core behavior is adequately disclosed.

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 efficient and front-loaded: it states the action first, then the returned payload, then the use cases. The field list is long but earns its place because there is no output schema. The redundant heading 'Get Place Details' is a minor blemish.

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 three parameters, no output schema, and no annotations, the description supplies the essential return-value list and use cases, so an agent can judge when to call it and what to expect. It could be more complete by pointing to performMapSearch as the way to obtain a placeId or by noting response formatting, but these are not blocking gaps.

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 documents all three parameters with 100% coverage, so the baseline is 3. The description adds only a small amount of context by saying the localization parameters are optional and by framing placeId as the lookup key; it does not provide additional parameter-level 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?

The description opens with a specific action and resource: it 'Fetches full Google Maps place data by placeId', immediately distinguishing this details-by-ID tool from sibling tools like performMapSearch, getMapReviews, getMapPhotos, and getMapPosts. The explicit field list (name, address, coordinates, phone, categories, hours, etc.) reinforces that this is the comprehensive place-details endpoint, not a focused sub-resource tool.

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 concrete application context—'local SEO audits, POI enrichment, lead generation, competitor mapping, and building location-aware agents'—which tells an agent when this enrichment tool is appropriate. It does not explicitly name sibling tools or state when not to use it, so it falls short of full 5-level 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?

The description discloses that the operation is a paginated fetch and enumerates the returned data. However, with no annotations provided, it does not mention authentication requirements, rate limits, or potential side effects, so it does not fully carry the behavioral transparency burden.

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 and well-structured. It covers what the tool does, what it returns, and relevant use cases in a compact two-paragraph format without unnecessary filler.

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 fairly complete for a read-only fetch tool: it names the resource, the identifier options, the pagination mechanism, the return fields, and practical use cases. It could be slightly stronger by explicitly addressing parameter precedence or edge cases, but it is adequate for effective use.

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 already covers all four parameters with descriptions, so the baseline is 3. The tool description adds minor context by mentioning 'by dataId or placeId' and 'nextPageToken pagination,' but it does not significantly enhance what the schema already conveys.

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 operation: fetch Google Maps Posts for a place by dataId or placeId. It also specifies the resource type and outlines returned fields, making it easily distinguishable from sibling tools like reviews or photos.

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 use cases such as small-business intelligence, local-marketing monitoring, and competitor activity tracking. It does not explicitly mention when not to use it or name alternative sibling tools, but the stated use cases give solid contextual 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?

No annotations are provided, so the description carries the transparency burden. It discloses pagination behavior, sort/filter options, mutually exclusive identifiers, and the per-review fields returned. It does not mention rate limits, errors, or read-only guarantees, but the core behavior is adequately described.

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 short title, a functional summary, a list of returned fields, and use cases. It is slightly repetitive in listing parameter names that already appear in the schema, but it remains appropriately sized and easy to scan.

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 provides enough context for an agent to select and invoke the tool correctly: purpose, key parameters, pagination token, and expected output fields. It is not exhaustive (no output schema or error scenarios), but it is adequate for the tool's 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?

Schema descriptions cover 100% of parameters, so the baseline is 3. The description adds meaningful context beyond the schema, particularly that either dataId or placeId should be set and that pagination is handled via nextPageToken, which improves parameter understanding.

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 fetches Google Maps reviews, with resource, operation, and key refinements (dataId/placeId, sorting, topic, language). It is clearly distinguished from sibling tools for photos, posts, place details, and 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?

The description gives explicit use cases such as reputation management, sentiment analysis, competitor review benchmarking, and feeding summarization/trust-score LLMs. It does not explicitly say when not to use it versus a sibling, but the resource-specific language makes the context 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?

No annotations are provided, so the description carries the full burden. It explains the search behavior, return fields, pagination via start, and the dependency between ll and start. It does not mention rate limits or error handling, but for a read-only search API this is acceptable.

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 the main purpose, and structured into three clear sentences: what it does, what it returns, and when to use it. It avoids unnecessary verbosity while covering essential details.

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 there is no output schema, the description compensates by enumerating the exact fields returned in the local pack list. It also covers pagination behavior, language/country/domain options, and practical use cases, making it complete for an agent to decide 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?

Every parameter in the schema has a description that adds significant meaning beyond the type. The ll parameter includes a format example and a conditional requirement, start explains pagination with concrete offsets, and gl/hl/domain include value ranges and examples.

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 runs a Google Maps search and returns the local pack list with specific fields. It distinguishes itself from sibling tools like place details, reviews, and photos by focusing on the search entry point.

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 lists concrete use cases (local lead generation, competitor density mapping, market expansion research) and hints at follow-up tools by mentioning feeding placeIds into Maps Place, Reviews, or Photos endpoints. It does not explicitly say 'when not to use', but the use-case list is strong enough.

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