user_agent_endpoint_v1_enrich_user_agent_post

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds cost information ('1 credit per request') but omits other behavioral traits such as response format, rate limits, or side effects. The cost disclosure is useful but limited.

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 exceptionally concise—two sentences with no redundancy. The primary purpose is front-loaded, and the cost is stated in a separate sentence, making it efficient and scannable.

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 tool with a single optional parameter and no output schema, the description is adequate. It covers the core function and cost. It could mention the response structure, but given the simplicity and lack of output schema, this is a minor 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 100%, and the schema already describes 'ua_string' thoroughly, including the fallback to the requester's UA. The description adds no additional parameter meaning beyond what the schema provides, aligning with the baseline of 3 for high coverage.

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 extracts 'detailed browser, OS, and device information' from a 'User-Agent string,' using a specific verb and resource. This distinguishes it from sibling enrichment tools (e.g., enrich_date, enrich_name) without ambiguity.

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?

While not explicitly stating when to use it vs. alternatives, the description's focus on User-Agent parsing makes its intended use obvious. It provides clear context of the tool's function but lacks explicit exclusions or alternative routing.

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