athena_gp_fitc_predict

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

With no annotations, the description carries the full transparency burden and does disclose meaningful behavioral traits: the prediction is deterministic, uses a FITC approximation, and exposes query-level error. However, it omits side effects, prerequisites, return structure, and the meaning of 'bounded GP reference,' leaving important behavioral ambiguity.

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 one-sentence description is efficient, front-loaded, and contains no filler. It packs the core purpose and distinguishing output into a compact statement, though the dense jargon and undefined 'bounded GP reference' reduce clarity.

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?

This is a complex tool with a nested features object, no output schema, and no annotations, but the description provides no details on parameter values, return shape, or usage context. The sparse description is insufficient for an agent to invoke it correctly without additional information.

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%, and the description does not explain any of the four parameters (context_key, features, inducing_count, include_observation_noise). The high-level FITC context provides no guidance on how to construct the features object, what context_key refers to, or how inducing_count affects predictions.

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 predicts using a deterministic inducing-point FITC GP approximation and exposes query-level error against an exact bounded GP reference. This specifies a unique verb, resource, and method, distinguishing it from sibling GP predictors like athena_gp_predict and athena_gp_sparse_predict.

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 the tool is for FITC-based prediction with error analysis against a bounded GP reference, but it does not explicitly state when to use it over alternatives or provide exclusion criteria. The guidance is implicit rather than actionable, leaving the agent to infer usage from the sibling tool list.

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