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

With no annotations provided, the description fully discloses behavioral traits: it is read-only, requires no auth, explains the matching fallback order, returns a family-level record with matchedVia flag, and returns found:false rather than guessing. This is comprehensive and honest.

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 serves a purpose. It front-loads the core function, then adds return details and matching rules. No redundant or filler content. The length is justified by the complexity of the matching behavior.

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?

Despite no output schema, the description enumerates the key returned fields (support status, End-of-Sale/End-of-Service-Life dates, runway score, bulletin URL) and explains edge cases like family-name matches and found:false. This is complete for a single-lookup tool and leaves no major ambiguity.

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%, so the baseline is 3. However, the description adds substantial meaning beyond the schema by explaining how part_number is matched (exact, punctuation-insensitive, Fortinet aliases, family fallback), which is not present in the schema. This helps users understand what values are acceptable.

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 looks up a single hardware part number or model name in the EOSL.ai database, with a specific verb ('look up') and resource. It also distinguishes from the sibling tool bulk_check by noting 'for many parts use bulk_check', making its single-item scope 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?

The description tells users when to use this tool (for a single part) and explicitly directs users with many parts to use bulk_check. It also clarifies matching behavior and the family-name fallback, which helps decide if this tool is appropriate vs. alternatives like search_models.

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