Classify AI and Web3 job signals

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

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses determinism, lack of provider contact, keyword-derived nature, and the caveat that signals are unverified. This add significant behavioral context that annotations alone do not provide.

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 two sentences, front-loaded with the core purpose, and includes only essential caveats. Every phrase adds value—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?

The description covers purpose, behavior, and reliability caveats, which is solid for a read-only classifier. However, with no output schema, it does not explicitly describe the return format or the structure of the confidence indicator it mentions, leaving a small but notable gap.

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 0%, and the description does not compensate by explaining which job fields (title, description, tags, etc.) are used for classification or how they influence signals. The only hint is 'supplied jobs,' which is minimal and does not clarify the input structure or semantics.

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 uses a specific verb ('identify') with a clear resource ('AI, agentic and Web3 domain signals') and scope ('in supplied jobs'). It clearly distinguishes the tool from siblings like jobscout_search_jobs and jobscout_deduplicate by emphasizing deterministic local classification of provided jobs.

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?

It clearly states when to use the tool (given jobs to classify) and adds important context: no provider contact and signals are only hints, not verified facts. However, it does not explicitly mention when not to use it or compare with alternatives, so it falls short of full exclusion guidance.

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