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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by explaining the ranking basis, the score range, the exclusion criteria, and that the exact rule appears in a returned `method` field. It does not describe the full return shape or explicitly state it is read-only, but for a ranking tool the disclosed behavior is substantive.

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?

Three sentences, each earning its place: the first states the ranking and metric, the second clarifies provenance and points to the `method` field, the third states exclusions. No filler or repetition; the most decision-relevant information is front-loaded.

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 simple two-optional-parameter ranking tool with no output schema, the description gives enough context: what is ranked, by what metric, with what exclusions, and where to find the exact rule. The only gap is that without an output schema, the exact return fields per company (e.g., company name, jobs count) are not spelled out.

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 description coverage is 100%, so the baseline is 3; the schema already documents `limit` and `min_jobs`. The description adds some contextual meaning by tying 'too few live listings' to `min_jobs` and explaining the AI score scale, but it does not materially expand parameter semantics beyond what the schema provides.

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 opens with a specific verb and resource: 'Ranks companies currently hiring on Level by how AI-native their open roles are.' It details the exact metric (average AI score, 0-100) and clarifies this is a Level-specific aggregation, not a third-party ranking. This clearly distinguishes it from siblings like search_jobs, which targets individual job listings rather than company-level rankings.

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 conveys clear usage context: use this tool when you want companies ranked by AI-nativeness of their live listings. It also states exclusions (companies with too few listings, job-board republishers). However, it does not explicitly mention when not to use it or name alternative sibling tools, so the routing guidance is clear but not fully explicit.

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