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

The description goes well beyond the readOnly/idempotent annotations by disclosing result tiers, headline verdict structure, dead-mark sampling, performance expectations, and a signed-in account requirement. It even includes measured timing for include_dead (150s then failed), which is highly useful for agent timeout decisions. There is no contradiction with 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 dense but every sentence earns its place: engine capabilities, output structure, parameter caveats, exclusions, timing, and authentication are all covered without filler. Key constraints are front-loaded, and the performance data is placed near the include_dead warning where it matters most.

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 compensates by explaining exactly what the agent will receive: four risk tiers, a four-level verdict with a one-line reason, and a dead-mark sample. It also covers practical call context such as runtime, authentication, and parameter tradeoffs, making the tool fully callable by an agent without further inference.

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?

Although the schema descriptions already cover 100% of parameters, the tool description adds crucial meaning: goods_description is tied to relatedness scoring with a concrete understatement risk, owner_name is explained as adding portfolio context, and include_dead gets behavioral warnings beyond schema defaults. This substantially enriches parameter understanding 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 leads with a specific verb and resource: 'Runs a scored trademark conflict (knockout) search over 14M USPTO records.' It names the matching engine and result structure, making the tool's identity unmistakable. It also distinguishes itself from domain coverage by pointing to check_brand_availability, so agents can tell 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?

Usage guidance is explicit: it states when to set include_dead (only if the user explicitly asks about dead/abandoned marks) and when not to ('LEAVE IT FALSE for availability / what would block me questions'). It also names check_brand_availability as the alternative for domain/web checks and highlights when goods_description is strongly recommended. This gives the agent clear decision rules rather than leaving usage to inference.

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