Antevo Trademark

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

The description discloses critical behavioral traits beyond the annotations: counts are from a mirror not the office, coverage varies, and stale offices are flagged in 'do_not_quote'. It even warns against misinterpreting low counts. This adds significant context that annotations alone (readOnly, openWorld, idempotent) do not convey, and there is no contradiction.

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: it starts with the core purpose, then usage, then a critical caveat. Every sentence contributes value, and the caveat is prominently flagged with uppercase and 'do_not_quote'. It is not overly long for the complexity of the tool, and the formatting aids readability.

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 is a read-only analytical tool with no output schema, the description covers the necessary context: what it returns, how to invoke it, and the important mirror/coverage caveat. It also instructs the agent on how to relay the information ('Relay that'), making it sufficient for the agent 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 carry the parameter semantics. It explains that nice_class is a numeric class (with examples), jurisdiction is an office code ('EM', 'US', 'GB', 'CH'), and months is a window. This adds meaning beyond the bare schema. However, it does not mention the default values (months=12, jurisdiction=''), which would have made it more complete.

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 purpose: 'Filing activity in a Nice class — volume by month and the busiest filers.' This goes beyond a simple restatement by specifying the resource (Nice class), the output (volume by month, busiest filers), and the scope, distinguishing it from sibling tools like opposition_window.

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 concrete usage instructions: 'Pass a Nice class (9, 42, 36…), optionally an office code ('EM', 'US', 'GB', 'CH'), and a window in months.' It also states what questions the tool answers, giving clear context. It does not explicitly mention when not to use it or compare with alternatives, but the use cases are well-defined.

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 declare read-only and non-destructive, so the description adds value by explaining the computation basis (whole register, lapses, class clustering, leverage), the output expectation (present evidence), and a legal disclaimer about not labeling squatters. This goes beyond what annotations offer.

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 but includes a purpose line, a computation basis, and behavioral caveats. It is front-loaded with the core idea and avoids unnecessary repetition, though the poetic phrasing adds some verbal flourish.

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 read tool with one parameter and rich annotations, the description provides sufficient context about data sources, behavioral constraints, and output expectations (evidence-based). It lacks explicit usage guidance and parameter specifics, but overall it is complete enough for an agent to understand the tool's role and 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 single parameter 'name' is only described as a string in the schema, and the description does not explicitly define what a valid name looks like (company vs. individual, exact name vs. partial), nor does it connect the parameter to the output beyond context. The description is about the holder generally, not about the parameter's format or behavior.

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 title and description clearly state this is a read operation on a trademark holder, with a specific verb (read) and resource (holder). It differentiates from sibling tools by positioning itself as 'the read on an adversary,' distinct from opposition_window and screen_mark.

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 use for understanding an adversary/holder but provides no explicit when-to-use or when-not-to-use guidance, nor does it reference alternative tools. The 'Free' note and 'adversary' framing give some context, but the guidance is not explicit.

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 mark it as read-only and idempotent. The description adds meaningful behavioral context: it covers twenty registers, includes the provision the window comes from, and highlights the key minority case where opposition runs after registration. It does not describe the exact output format or fallback for unknown offices, but this is minor for a simple lookup.

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 a direct answer to the tool's purpose, followed by valuable domain context and input guidance. Every sentence contributes meaning, with no redundancy or 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?

For a one-parameter read-only lookup, the description covers purpose, the critical pre/post-registration distinction, input flexibility, and data coverage. The only notable gaps are lack of explicit return format and undefined behavior for offices not in the twenty registers, but overall it is sufficient for an agent to select and invoke 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?

The schema only provides the parameter name 'office' with no description. The tool description fully compensates by specifying that it accepts a country name, an office name, or a two-letter code, and gives examples ('japan', 'JP', 'EU'). This adds essential 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 states exactly what the tool does: it tells how long you have to oppose a filing in a trademark office and from when. It also uniquely differentiates itself by addressing the pre-registration vs post-registration opposition trap, making it clearly distinct from sibling tools.

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 clear context on when to use the tool: for any trademark office, and it warns against applying one office's rule to another. It also explains valid input forms. However, it does not explicitly name alternatives or state when not to use it, though the sibling names imply some 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable specifics: it returns every near mark with a similarity tier and which registers were checked, and it is free/no-account. It also clarifies the data comes from live registers, enhancing behavioral understanding beyond 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 description is concise with no filler. The first sentence front-loads the core action, followed by output details and usage guidance in just two short paragraphs. 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?

With no output schema, the description explains the return value (near marks, similarity tier, checked registers) and access requirements (free, no account). It also gives usage context. For a one-parameter read-only tool, this covers all necessary 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 only 'mark' as a string with no description, and schema coverage is 0%. The description compensates by equating the parameter to a 'brand name', providing the necessary semantic meaning for the single simple parameter.

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 'Screen a brand name against the live trademark registers,' clearly identifying the verb (screen), resource (brand name), and scope (trademark registers). It also distinguishes this tool as the first step for checking name availability, setting it apart from sibling tools.

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 explicitly says 'This is the first thing to run when someone asks "is my name taken",' giving clear contextual usage. It does not name alternative tools, but the phrase 'first thing to run' implies a workflow context where other tools follow.

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds extra behavioral context: 'Free, no account, no database' and that the tool reports how similar businesses file with a closing caveat. This goes beyond annotations, though it doesn't enumerate all output details.

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 efficiently written, with each sentence adding value: purpose, problem context, usage guidance, output behavior, and a caveat. It is well-structured and front-loaded with the core purpose.

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 a one-parameter tool with no output schema, the description is remarkably complete. It explains what the tool returns (classes and reasoning), when to use it, why it matters, and includes the caveat about legal counsel. No critical information is missing.

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 has 0% parameter description coverage, so the description must compensate. It does: 'a business like this' and 'when someone describes a business' imply the single 'description' parameter is a business description. The meaning is clear, though not formally structured or exemplified.

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 what the tool does: it returns the Nice classes a business files in. It distinguishes from siblings by explicitly contrasting with 'is my name taken' and naming screen_mark as a related but different tool. The verb is implied but the resource is specific.

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 usage guidance: 'Run this BEFORE screen_mark when someone describes a business rather than naming a class'. It also explains why, noting the classes are what make a screen meaningful. This clearly indicates when to use this tool over alternatives.

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