constituency_parse

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

With no annotations, the description carries the behavioral burden and does disclose the return shape (bracketed trees and node labels) plus the model prerequisite. It does not explain failure behavior when the required stanza processor is unavailable, nor any performance or rate-limit considerations.

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 three compact sentences with no wasted material. Purpose, return values, and the model prerequisite are front-loaded and easy to scan.

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 the description need not fully document return values, but it helpfully summarizes them. It remains incomplete on parameter semantics and on how this tool relates to sibling parsers, which matters for correct tool selection.

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%, so the description should compensate for the three undocumented parameters. It only indirectly hints at lang through 'the language's stanza models' and says nothing about max_trees or accepted lang values, leaving key parameter semantics unclear.

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 names a specific operation and resource: phrase-structure/constituency parses via stanza. It clearly distinguishes this from generic parsing by specifying constituency trees and the Stanford/stanza engine, though it does not explicitly name dependency_parse as the contrasting sibling.

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?

Usage is implied by the tool name and the phrase-structure focus, and the description adds a prerequisite about needing stanza models with the 'constituency' processor. However, it does not explicitly say when to choose this over dependency_parse or other siblings, nor does it describe exclusions.

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