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

Annotations already declare read-only, closed-world, idempotent behavior. The description adds that it searches live UK listings, which is useful context beyond annotations. No contradiction. Could mention result limit or ordering but overall adequate.

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?

Two concise sentences with no wasted words. Front-loads the core purpose and adds relevant details in the second sentence.

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?

No output schema exists, so the description should cover return structure. It only says 'Returns matching live UK listings' without details on fields, scoring, pagination, or result count. For an AI search tool, this is insufficient.

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 baseline is 3. The description adds an example query and lists handled criteria (budgets, headcount, etc.), providing meaningful semantics beyond the schema's brief description.

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 searches FrankSpace workspaces using plain-English queries, listing supported criteria (budgets, headcount, etc.) and notes it returns live UK listings. It distinguishes from siblings by emphasizing natural language vs structured search.

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 implies use for natural language queries (the same AI parser as the app) but does not explicitly exclude structured search or individual workspace lookup. It provides clear context for when to use but lacks exclusion guidance.

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