lookup_part

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

With no annotations, the description fully discloses behavior: read-only, no auth, matching algorithm (exact, punctuation-insensitive, Fortinet aliases), family-name fallback with matchedVia flag, and found:false behavior. No contradictions with annotations since none exist.

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 information-dense but well-organized, front-loading purpose and scope, then return fields, then matching details. Each sentence contributes unique value with no redundancy or fluff.

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's complexity, the description is remarkably complete: input, output fields, matching behavior, fallback, no-auth requirement, and no-guarantee case. Without an output schema, it sufficiently prepares an agent for invocation and interpretation.

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 schema covers 100% of parameters, the description adds meaning beyond it: vendor restricts match, part_number can be a model name, and matching is exact then fallback. It gives concrete examples (FG-60E, WS-C3850) and explains the vendor hint's role, greatly enhancing schema info.

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 the tool looks up one hardware part number or model name in the EOSL.ai database, using a specific verb and resource. It clearly distinguishes from siblings by mentioning bulk_check for multiple parts and specifying the matching fallback behavior.

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?

Explicitly says 'for many parts use bulk_check', an alternative tool. It also describes when a family-name match occurs and that no match returns found:false, giving agents clear conditions for use versus not using the tool.

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