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

With no annotations provided, the description carries the full burden, and it does so thoroughly. It discloses that the operation is read-only and requires no auth, lists all returned fields (status, dates, score, URL), and details the matching algorithm including edge cases like family-name fallback and found:false rather than guessing. This is exceptional transparency for a read tool.

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 yet dense: it front-loads the primary purpose, then lists return fields, then details matching logic in a logical sequence. Every sentence earns its place, and there is no redundant or filler content. The structure makes it easy for an agent to parse quickly.

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 no output schema, the description completely explains the return values and all edge cases: matched SKU, family-name match, vendor restrictions, and found:false. It also covers the alternative for bulk checks. Nothing an agent needs to invoke the tool correctly or interpret results 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 description coverage is 100%, so the schema already fully documents both parameters (part_number and vendor) with examples and behavior. The description adds value by explaining the Fortinet alias resolution and family-name fallback, but these are tool behaviors rather than parameter-specific semantics. Baseline 3 is appropriate since the description does not meaningfully expand on what the schema already provides.

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 ('look up'), a resource ('hardware part number or model name'), and a database ('EOSL.ai'). It also distinguishes itself from the sibling bulk_check by explicitly saying 'for many parts use bulk_check', so an agent can tell them apart without opening schemas.

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 names bulk_check as the alternative for many parts, giving an explicit when-not condition. It also explains the matching behavior (exact, punctuation-insensitive, aliases, family-name fallback) and the found:false case, which helps the agent decide when this tool is appropriate vs. search_models or get_family.

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