Detect AI Tooling

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

Beyond the annotations (readOnlyHint, idempotentHint), the description discloses many behavioral traits: returns specific fields (ai_maturity, vendors, categories, llms.txt status, robots.txt policy, evidence strings), how bot challenges are handled (blocked=true at low confidence), and that public data requires no login but costs credits. This is rich, useful 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 a well-organized paragraph that front-loads the core purpose, then details return values, an edge case, and operational constraints. Each sentence adds distinct value, and the length is appropriate for the tool's complexity.

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?

With no output schema, the description must explain return values and does so thoroughly: it lists the ai_maturity tiers, detected vendors/categories, llms.txt status, robots.txt policy, evidence strings, and the blocked field. It also covers the 'flat Clay-ready JSON' format, making the tool's behavior fully understandable without an output schema.

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 schema description coverage is 100%, so all parameter semantics are already documented. The tool description adds no new parameter-specific details beyond what the schema already states, though it does reinforce the overall domain-targeting concept. Baseline 3 is appropriate because the schema does the heavy lifting.

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 tool's function: 'Given a company domain, determine how far that company has actually gone with AI' and defines the exact output tiers (none, declared, deployed, commercialized). This makes the purpose specific and unambiguous, even without siblings to distinguish from.

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 provides clear usage context: it takes a domain or domains, supports batch mode, and explains the effect of check_pricing (capping at 'deployed'). It also mentions operational prerequisites (APIFY_TOKEN, Apify credits) and the bot-challenge edge case. No explicit exclusions or alternatives are given, but with no sibling tools this is not a gap.

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