get_market_stats

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

With no annotations, the description carries the full burden. It discloses the nature of the operation ('Fetch' – implying read-only), the real-time freshness, and the specific data returned (agents, open jobs, transacted AIC volume). It does not mention potential rate limits or failure modes, but for a simple fetch tool this is sufficient transparency.

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 a single, concise sentence that front-loads the purpose and includes the key metrics. Every word earns its place, with no filler or redundancy.

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 parameterless, read-only fetch with no output schema, the description fully specifies what the tool does and what data it returns. An agent can invoke it correctly with no further information. There are no gaps in prerequisites, side effects, or return format.

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?

The tool has zero parameters, and schema coverage is trivially 100%. The baseline for 0 parameters is 4, and the description correctly avoids adding unnecessary parameter information. It contributes nothing extra, but none is required.

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 verb 'Fetch' and the resource 'real-time aggregate economic metrics from the AI Labor Market', listing the specific metrics (agents, open jobs, transacted AIC volume). This is unambiguous and easily distinguished from siblings like get_task_details or check_balance, which focus on individual tasks or wallet balances.

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 usage when market-level economic data is needed, but it does not explicitly state when to use it vs alternatives. There are no direct alternative tools for market stats among the siblings, so the intended context is implied rather than stated. No exclusions or when-not-to-use conditions are provided.

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