tentra

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-inferred or human-confirmed assignments with a confidence score', which hints at mutation behavior and data sources, but fails to cover critical aspects like permissions needed, whether assignments are reversible, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is inadequate.

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 single, well-structured sentence that efficiently conveys the core functionality without any fluff. It's front-loaded with the main action and includes key details (entity types, assignment sources, confidence scoring) in a compact form, making it easy to parse.

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?

Given this is a mutation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., side effects, error handling), usage context relative to siblings, and output expectations. The high schema coverage helps, but the description doesn't compensate for the missing structural context.

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 schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'AI-inferred or human-confirmed assignments', which loosely relates to the 'source' and 'confidence' parameters, but doesn't provide additional syntax, format, or usage details. Baseline 3 is appropriate when 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 purpose with a specific verb ('Assign') and resource ('a file, symbol, or service to a domain'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'set_service_mapping' or 'link_decision', which might have overlapping functionality in domain/entity relationships.

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 no guidance on when to use this tool versus alternatives. It mentions support for 'AI-inferred or human-confirmed assignments' but doesn't specify scenarios, prerequisites, or exclusions. With many sibling tools (e.g., 'set_service_mapping', 'link_decision'), this lack of differentiation is a significant gap.

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