UGC VZ Creator Search

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds important behavioral context: it never returns private contact details, specifies how those are delivered, and defines the scope of the public profile. Minor gaps remain around error behavior or exact response shape, but the privacy boundary is well 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?

Front-loaded with the main verb and resource, then a comprehensive field list, followed by the privacy caveat and usage guidance. The English/German duplication nearly doubles length but is still organized and purposeful; no filler beyond that.

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 single-parameter read-only tool with no output schema, the description covers what the tool does, what it returns, where the input comes from, how to use it relative to siblings, and a critical privacy limitation. Nothing material is missing 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 description coverage is 0%, so the description must compensate for the single parameter. It adds meaning by stating creator_public_id comes from a previous search_creators result, and the pattern in the schema provides the format. This is sufficient for an agent to populate the parameter 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?

States a clear verb ('Returns') and resource ('public profile of a single UGC creator'), identifies the exact input origin ('creator_public_id from a previous search_creators result'), and enumerates the returned fields. This distinguishes it from search_creators (search vs. single profile) and request_outreach.

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 to use before request_outreach to inspect a match more closely, and ties input to a previous search_creators result. It also clarifies that private contact details are not available here and surface only via request_outreach, which prevents misuse.

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?

Beyond the read-only annotation, the description discloses that the tool never returns private contact details, enumerates all four possible statuses, includes the 48-hour failure timeout, and notes that timestamps are returned. These are meaningful behaviors an agent needs to know.

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 core purpose is front-loaded and the statuses are compactly enumerated. The bilingual German version approximately doubles the length without adding new information for an AI agent, so it is slightly less concise than ideal.

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 single-parameter, read-only polling tool with no output schema, the description covers when to call, all statuses, terminal conditions, polling guidance, and privacy behavior. An agent has enough to call it correctly and interpret its result.

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 only provides type and length constraints for request_id. The description adds crucial semantics by explaining that request_id refers to a contact request previously triggered with request_outreach. It does not go into how the ID is obtained, but the connection to request_outreach is sufficient for correct invocation.

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 verb ('Returns'), a specific resource ('status of a contact request previously triggered with request_outreach'), and the identifier (request_id). It distinguishes itself from siblings by stating that it never returns private contact details and only reports lifecycle status.

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 an explicit usage pattern: 'Call repeatedly after request_outreach until the status is completed or failed.' This clearly communicates when to use the tool, though it does not explicitly contrast it with sibling tools like get_creator or search_creators.

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?

Annotations already convey readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds meaningful behavioral context by stating the response 'Contains no creator or contact data' and that the vocabulary is 'currently valid', implying dynamic values. It does not contradict the annotations.

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 English portion is front-loaded and crisp, clearly stating purpose and usage in three sentences. However, the entire description is repeated in German, doubling the length and adding no new semantic information for an English-reading agent. This redundancy prevents a higher score.

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 0-parameter, read-only vocabulary tool, the description covers the returned content categories and the exact conditions for calling it. It lacks a detailed response schema, but given the simple nature of the tool and the absence of an output schema, the description is sufficiently complete 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?

The tool has no parameters, so the baseline is 4. The description still adds value by identifying the filter values (city, topics, human_verification_level_min) that the vocabulary informs for search_creators, which helps the agent understand the data's purpose even though this tool itself takes no parameters.

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 a specific verb and resource: 'Returns the currently valid vocabulary for search_creators' and enumerates the content categories (topics, industries, cities, human_verification levels, pricing note). It also differentiates itself from sibling tools by explicitly noting it 'Contains no creator or contact data', which separates it from get_creator and request_outreach.

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 when-to-use guidance: 'Call before a first search_creators request or when unsure about valid filter values (city, topics, human_verification_level_min).' This clearly indicates the condition for invocation. It does not explicitly name alternatives, but as a 0-parameter preparatory tool, alternatives are not necessary and the tie to search_creators is sufficient.

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?

Beyond the annotations (readOnlyHint=false), the description discloses a genuine side effect: the call triggers a real e-mail and should only be used for serious, genuine requests. It also sets expectations that no private contact data is returned, which is not inferable from the schema or annotations.

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 English text is front-loaded and logically structured: action, requirements, return value, prerequisites, optional parameters, flow, warnings, and terms. However, the full German translation roughly doubles the length without adding new functional information for an agent reading the English description.

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 covers side effects, the return value (request_id), prerequisites, the canonical flow, and the absence of private-data returns, all of which are essential because there is no output schema. It also includes operational guardrails such as no bulk requests, no test calls, and applicable terms of 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?

With 0% schema description coverage, the description compensates by naming all five parameters: name and email must belong to the requesting brand, creator_public_ids must come from a previous search, and message and search_query are optional free-text context for UGC VZ. It does not repeat schema constraints but adds the missing semantic layer.

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 precise verb and resource: it 'triggers a deliberate brand request' and explains the real-world consequence (UGC VZ forwards creator contact details by e-mail). It also differentiates itself from sibling search/get tools by stating it returns only a request_id and no creator or private contact data.

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 preconditions ('Creators must have been found via search_creators or get_creator before this call'), a canonical flow (search_creators -> get_creator -> request_outreach -> get_outreach_status), and clear exclusions ('No bulk requests, no test calls'). This leaves little ambiguity about when the tool should and should not be used.

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?

Beyond the read-only and non-destructive annotations, the description discloses meaningful behavior: results never contain private contact details, a language model structures queries server-side, and verification levels are derived from profile data. This adds practical behavioral context that an agent cannot infer from annotations alone.

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 English portion is concise, front-loaded with the core purpose, and logically organized from main parameter to optional filters to behavioral caveats. The full German repetition doubles length, but this is justifiable for a DACH-focused tool and carries no 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 covers the search concept, the free-text query mechanism, optional filters, privacy constraints, and downstream sibling tools. With no output schema, it does not describe the return shape in detail, but for a directory search this is a minor gap since get_creator is provided for deeper details.

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 carries the burden of explaining parameters. It does explain query with an example, city as substring, topics as at least one match, and human_verification_level_min values 0 and 1 with a pointer to get_vocab. However, it omits max_results entirely and does not define the possible value 2 for human_verification_level_min.

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: 'Searches the UGC-VZ directory of real, verified UGC creators in the German-speaking region (DACH).' It also separates this tool from siblings by referencing get_creator for details and request_outreach for contact requests, making the purpose unmistakable.

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 explains that query is free text and the main path, provides a concrete example, and lists optional filters. It explicitly directs when to use alternatives: 'Use get_creator for details on a match; request_outreach for a contact request.' This gives clear usage guidance beyond a mere definition.

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