ai_extract

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

No annotations are provided, so the description carries the full behavioral burden, yet it only says an LLM is involved. It omits non-determinism/cost implications, latency, failure behavior when output does not match the supplied JSON Schema, and any truncation semantics beyond what the schema default states.

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?

A single front-loaded sentence with no filler. It is efficient, though arguably too terse for an LLM-backed tool with non-trivial behavior.

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?

An output schema exists, so return format needn't be explained, but this is a complex LLM extraction tool with nested schema input and no annotations. The description leaves out model behavior, cost/rate considerations, and what happens on schema-conformance failure, so it is not complete enough for confident invocation.

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; each parameter already has a description. The tool description adds nothing about the meaning or interaction of text, prompt, schema, or max_chars, so no credit above baseline.

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 (structured extraction) and resource (report section text) with the added qualifier that it is LLM-driven, which distinguishes it from purely mechanical extraction. However, it does not name or differentiate itself from close siblings like extract_section or extract_indicators, so the agent must infer the boundary.

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?

There is no when-to-use or when-not-to-use guidance, and no alternative is named. Given several overlapping siblings (extract_section, extract_indicators, extract_indicators_by_position), the absence of routing guidance is a real gap.

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