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

With no annotations provided, the description alone must disclose behavior, and it does: it caps results at 20, explains ranking by AI score and recency, and explicitly states that results link to the canonical jobsbylevel.com page and never to the employer's ATS. It also briefly defines the AI Level scale, which is not in the schema. It stops short of describing response structure or error/pagination behavior, but the key behavioral surprises are covered.

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 compact paragraph that front-loads the purpose before moving to filters and result behavior; every sentence contributes either purpose, filtering semantics, or return constraints. No filler or repetition of the schema's parameter descriptions.

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 seven optional parameters, no annotations, and no output schema, the description covers the source, filtering dimensions, result cap, ranking order, and link behavior, which is enough to select and invoke it. It does not spell out the result object shape, but in the absence of an output schema, the description still gives the essential runtime constraints an agent needs.

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 the baseline is 3; the description adds value on top by explaining the AI Level scale ('AI Level 1 = AI is not the work, to AI Level 4 = AI is the job') and grouping the seven parameters into filter categories. It also clarifies ranking/result-limit semantics that give parameters like ai_level_min/max meaning beyond their type constraints.

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?

States a specific verb ('Search') and resource ('job listings published on Level'), and distinguishes the activity from siblings like get_job and market_stats by framing itself as the broad filtered search tool. It also adds domain context (AI Level ratings) so an agent understands exactly what kind of listings are searched.

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 through 'Search job listings' and enumerates filters, but it never explicitly tells an agent when to prefer search_jobs over a sibling like get_job or how to decide among them. It provides no exclusion or 'when not to use' guidance, so the agent must infer the division of labor from names.

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