Kansas City developer job-market stats

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: salary statistics are suppressed under 20 data points, and it states the data is live and sourced from kcdevs.com. This goes beyond a mere operation name and informs the agent of data-quality caveats.

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?

A single, well-structured sentence that front-loads the core purpose and then lists the return contents in a colon-delimited list. Every clause adds value; there is no filler or repetition.

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 there is no output schema, the description fully specifies the return payload: active job count, hiring companies, salary statistics (with suppression rule), demand by category, in-demand skills, and location mix. This is sufficient for an agent to know exactly what it will receive, plus the data source for trust.

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 takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to add about parameter meaning, and it does not attempt to invent any.

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 a specific verb ('Get'), a precise resource ('live snapshot of the Kansas City software-developer job market'), and enumerates the exact data points it returns. It is clearly distinct from sibling tools (get_job, list_companies, search_jobs, get_company) which target individual records, while this targets aggregate market statistics.

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 gives clear context: it is a market-level snapshot, implying use when aggregate statistics are needed rather than individual jobs or companies. However, it does not explicitly name alternatives or state when not to use it, so it falls short of the top score.

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