Research AI Employee Market

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

Annotations (readOnlyHint=false, openWorldHint=true) already communicate the non-read-only, open-world nature. The description adds behavioral detail about using both Arabic and English queries and producing a 'clearly caveated' score, but does not disclose runtime, dependencies beyond schema, or potential side effects beyond what annotations imply.

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 sentences, front-loaded with scope and outputs, no redundant wording. Every sentence earns its place, making it highly efficient and easy to scan.

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?

The description covers core inputs and outputs at a high level, which is adequate for a research tool with annotations present. However, it omits results_per_query semantics and does not detail what 'market metrics' or 'evidence' include, leaving some ambiguity without an output schema.

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 only 25% (only category_id has a description), so the description compensates by explaining taxonomy category, markets (EG/SA/UAE), and languages (Arabic/English). However, results_per_query is not addressed in the description. This provides meaningful added meaning for three of four parameters.

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 researches one taxonomy category across EG/SA/UAE using Arabic and English queries, and specifies outputs (market metrics, evidence, opportunity-screening score). This verb+resource+scope structure distinguishes it from sibling tools like compare_ai_employee_opportunities and get_market_evidence.

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 a single-category, multi-market research use case but does not explicitly state when to prefer it over alternatives (e.g., compare_ai_employee_opportunities) or when not to use it. No exclusions or alternative tool references are provided.

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