One1 Trademark Risk Check

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

No annotations are provided, so the description carries the full behavioral burden, and it does disclose substantive caveats: coverage tiers (full/partial/thin/very-thin), stale cutoff dates with examples, and the resulting invalidity of negative matches. It does not cover auth/permission requirements or rate limits, so it stops short of a 5.

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?

It is front-loaded with the returned fields, then the usage rule, then the parameter semantics — a sensible order with little waste. The field enumeration and the parenthetical date examples are dense but each item earns its place; it is slightly long but not padded.

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?

With no output schema and one undocumented parameter, the description supplies both the return-field inventory and the parameter meaning, plus the interpretive rule for stale/incomplete coverage. An agent has everything it needs to call this correctly and use the result to gate later 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 coverage is 0%, so the description must compensate, and it does: country is defined as an ISO 2-letter code with examples (US/CN/KR/BR/…) and the default behavior (empty returns all jurisdictions). This fully documents the single parameter beyond the bare schema, though it adds no format edge cases.

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 (query One1's per-jurisdiction data coverage) and enumerates exactly what the response contains: text line counts, data cutoff date, coverage tier, whether clearance conclusions are valid, graphic-library searchability, and plan threshold. This clearly separates it from the sibling search tools (trademark_text_search, graphic_trademark_search, etc.), which it is meant to precede.

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 explicit sequencing guidance ('check the target jurisdiction's ability to support a conclusion before calling other tools') and a concrete exclusion rule: when scope≠full or data_as_of is stale, a 'no match' does not mean the mark is registrable. This tells the agent both when to use it and how to interpret downstream results.

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 full disclosure burden. It does disclose the matching mechanism (CLIP visual similarity plus directory weighting), that image_url must be publicly reachable, and the return shape, but says nothing about permissions, rate limits, cost, or that coverage is limited to US marks (despite a country parameter).

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-loads the core capability (以图搜图) before the use case, then lists parameters and return values compactly. It is appropriately sized for the tool's complexity, though the parameter/return block reads as a dense run-on rather than cleanly separated lines.

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?

No output schema exists, so describing the return shape ({count, results:[{mark, visual_similarity, risk_level, applicant, legal_status}]}) is genuinely necessary and provided. Combined with parameter notes and the stated use case, an agent has enough to invoke the tool correctly, with only country the gap.

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%, so the description must compensate, and it does for image_url (public URL, PNG/JPG), nice_class (optional Nice classification), and limit (default 10). The country parameter is never mentioned, leaving one of four parameters undocumented in both schema and 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?

States a specific verb and resource: reverse image search (以图搜图) returning visually similar US graphic trademarks. The image-based retrieval distinguishes it implicitly from the sibling trademark_text_search, and the seller use case (checking whether a logo collides with a registered graphic mark) is explicit.

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?

Clearly states the intended scenario: sellers checking their own Logo/pattern against registered graphic trademarks. It gives a concrete context but names no alternative sibling or condition under which a different search (e.g. trademark_text_search) would be preferred, so it does not reach explicit when/when-not 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return structure (summary buckets, tro_count, hits with mark/risk_level/applicant/tro_alert), which is real behavioral value, but says nothing about accuracy limits, data source, latency, or authorization needs.

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 core action, followed by use case, parameter notes and return shape in a compact, scannable block. Every line contributes; 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?

With no annotations and no output schema, the description usefully supplies the return structure and the two key parameters. It is nearly complete; the undocumented country parameter is the main gap.

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 explains text ('listing 标题或文案,可整段') and nice_class ('尼斯商品类别号,可选'), but the third parameter, country (default US), is never mentioned — an agent would not know it can target non-US registries.

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+resource (scan a listing title/copy) and a clear scope: find US-registered trademark words and group them by risk. The '发品前一键自查' framing implicitly separates it from the per-term siblings like trademark_text_search, though no sibling is named explicitly.

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?

Gives a clear usage context — pre-publication self-check of an entire listing title for infringing marks. However, it names no alternatives (e.g. trademark_text_search for single-word lookups) and states no when-not conditions.

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 burden and adds useful behavior: it names the index used (idx_serial) and quotes sub-millisecond latency. It does not discuss permissions or failure behavior (e.g., what 'found: false' implies), so not a 5.

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 definition is front-loaded with purpose, then usage, then param, then return shape. It is efficient, though the inline return-shape summary is somewhat verbose for the value it adds.

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?

With no output schema, the description usefully sketches the return shape ({serial, found, result:{mark, legal_status, applicant, risk_level}}), which is important for an agent. The unmentioned 'country' parameter is the main remaining gap.

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%, so the description must compensate. It documents 'serial' with format guidance and an example ("78414956"), but the 'country' parameter is never mentioned in the text, leaving half the parameters undocumented.

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 (查/lookup) and resource (商标 by serial number) and emphasizes exact matching, which implicitly distinguishes it from text-search siblings. It does not explicitly name an alternative sibling, keeping it at 4 rather than 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?

It gives a clear when-to-use cue: when the serial number is already known (e.g., from an infringement notice). No explicit when-not or named alternative sibling is provided, so it falls short of 5.

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 full burden and provides meaningful context: it explains what is_enforcer=true means (a hit in the enforcement seed library), strongly recommends avoidance, and includes a legal disclaimer. It does not explicitly state that the operation is read-only or mention auth/rate limits, but for a profile lookup these are largely implied.

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, then usage, parameter meaning, return fields, and a disclaimer. The return field listing is necessary because no output schema exists, and the text is not overly verbose, though it could be slightly tighter.

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 no output schema and no annotations, the description provides the return fields and explains the owner parameter, but it omits any explanation of the country parameter and its effect on results. This gap leaves the agent without full guidance on how to use the tool 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. It explains the required owner parameter with examples ('Nike', 'YETI'), but completely omits the country parameter and its default, leaving half the parameters undocumented.

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+resource: it aggregates a brand's US trademark portfolio and determines whether it is a high-frequency TRO/Schedule A enforcer. This distinguishes it from search-oriented siblings, but it does not explicitly name or contrast with any sibling tool, so a 4 rather than 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?

It clearly states the usage context: for sellers to assess whether a brand aggressively enforces and whether to avoid it. It gives no explicit when-not-to-use conditions or alternative tools, so it falls short of a 5.

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 full behavioral burden. It usefully discloses defaults (live_only False, limit 20) and sketches the return shape, but says nothing about authorization, rate limits, or result caps beyond the default limit.

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 core action, then use cases, then parameters, then return shape. Terse and wasted-motion-free, though the use-case line is somewhat prose-heavy relative to parameter detail.

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?

Although no output schema exists, the description summarizes the return object (owner, count, results with mark/class/status/risk/tro_alert), which is the most valuable completeness element. The main gap is the undocumented country parameter.

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%, so the description must compensate. It explains owner (with an example), live_only, and limit, but the 'country' parameter (default US) is never mentioned even though its existence is non-obvious — the description only implies US scope via prose, leaving one of four parameters undocumented.

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 and resource: search US trademarks under a given owner/applicant. The purpose is unambiguous. However, it never distinguishes itself from the closely related sibling 'trademark_owner_profile', which an agent could easily confuse with this 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?

It provides concrete usage scenarios (competitor owner profiling, portfolio review, identifying high-frequency enforcers), which implies when to reach for it. But there are no explicit alternatives or exclusions, especially versus 'trademark_owner_profile' and 'trademark_text_search', which share overlapping intent.

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 exist, so the description carries the full burden, and it does substantial work: it warns that `count` equals limit and is NOT the conflict count, that judgments should use `has_conflicts`/`conflict_count`, and that a false `coverage.clearance_ok` means 'no conflict' is not clearance. It omits auth/permission and rate-limit behavior, but the interpretation caveats are exactly the kind of non-obvious behavior an agent needs.

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 purpose, then parameters, then the critical interpretation warnings. The warning block is long but each line is load-bearing; the trailing return-shape list is somewhat redundant filler given no output schema exists.

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?

With no annotations and no output schema, the description supplies both the safety/interpretation context and an inline sketch of the return object (query, conflict_count, has_conflicts, count, results, coverage). Only the undocumented `country` parameter and absent sibling routing keep it from being 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, and it documents query (with an example), nice_class (with an example and its effect on risk re-ranking), live_only (meaning and default) and limit (default). It leaves the `country` parameter (default 'US') undocumented, which is the one clear gap for a 5-parameter tool.

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 and resource (search US trademark registry for conflicting marks by sound/form/meaning similarity) and adds the practical framing of checking a brand name before launch. It never names or contrasts with siblings such as graphic_trademark_search or trademark_lookup_by_serial, so the differentiation is implied by the tool name rather than stated.

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 when to use it: before a seller names a product, to check for conflict with registered marks. It gives no exclusions or named alternatives (e.g. when to prefer graphic_trademark_search), but the context is unambiguous.

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