athena_gp_resolution_route

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

With no annotations, the description carries the transparency burden. It discloses the selection logic (shallowest tested representation, decision-margin constraint) but omits side effects, state changes, or what 'tested' entails. Adds some context but not rich detail.

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 dense sentence that is front-loaded and contains no filler. It manages to convey purpose and a key constraint efficiently, though it sacrifices clarity for brevity.

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?

For a tool with 5 parameters, no annotations, and no output schema, the description is far too sparse. It leaves critical questions about parameter meanings, return values, and operational semantics unanswered, making it inadequate for correct 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 coverage is 0%, so the description must explain parameters. It only loosely maps to 'actions' (action/query set) and 'margin_safety' (decision-margin error rule), leaving context_key, inducing_counts, and include_observation_noise unexplained. Insufficient compensation for low schema coverage.

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 chooses a representation ('shallowest tested FITC/full-GP') based on decision preservation, using specific verbs and resources. It distinguishes from GP prediction/observation siblings by focusing on representation selection, though the jargon limits accessibility.

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 implies usage when needing a GP representation that preserves decisions on a given action/query set, and mentions a decision-margin error rule. However, it gives no explicit alternatives or when-not-to-use guidance, relying on inference.

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