athena_gp_sparse_predict

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals the internal method (deterministic farthest-point subset) but does not mention expected side effects, whether this is a read-only operation, what output or comparison metrics are returned, or how the result is presented. The phrase 'compare the result' hints at behavior but leaves the actual behavior underspecified.

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 sentence, front-loaded with the primary verb 'Approximate'. It contains no filler and wastes no words, making it very concise and readable.

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?

The tool has a moderately complex purpose, no annotations, and no output schema. The description is too sparse to fully convey the input expectations, the meaning of the comparison, or the return format. An agent would be left guessing about the details of the 'exact bounded GP reference' and how results are reported.

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 by explaining the parameters. It does not mention 'context_key', 'features', 'inducing_count', or 'include_observation_noise' explicitly. The mention of 'deterministic farthest-point subset' loosely relates to 'inducing_count', but the description adds no concrete parameter guidance.

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 identifies the tool's action: approximate the current GP using a deterministic farthest-point subset and compare against an exact bounded GP reference. This distinguishes it from sibling tools like athena_gp_predict or athena_gp_fitc_predict by naming a specific approximation strategy and a comparison target, though it still leaves some ambiguity about what the 'bounded GP reference' is.

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?

No explicit guidance is provided about when to use this tool versus sibling tools. The description implies a comparison/validation use case but does not state prerequisites, exclusions, or alternatives. An agent would not know under what conditions to choose this over athena_gp_fitc_predict or athena_gp_predict.

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