pilot_summary

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

With no annotations, the description carries full burden and does well: discloses latency (10-30s), retry on timeout, prose output, and truncation avoidance. It lacks deeper context like auth or rate limits, but the provided behavioral expectations are valuable and specific.

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?

Two sentences, front-loaded with the core purpose, and every clause adds value. The structure efficiently covers purpose, use cases, and key behaviors without 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 tool has meaningful complexity (latency, retry, truncation, output format) and the description covers these well. It lacks an example of the prose output or handling of specialist-specific quirks, but for a summary tool it is sufficiently complete.

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 no extra meaning beyond the schema; it merely implies 'question' is used to guide synthesis but this is already in the schema. The description does not enrich parameter understanding.

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 returns an LLM-synthesized digest from a specialist, explicitly distinguishing it from raw /data and contrasting with pilot_query's truncation. The verb 'Get' and resource 'digest' are specific, and the sibling differentiation is strong.

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 explicitly says 'Use when you need a single answer from a large dataset' and names the alternative pilot_query, explaining why (truncation). This gives clear when-to-use and exclusions, making it a model of guidance.

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