athena_regime_geometry_observe

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

With no annotations, the description carries the transparency burden. It discloses that the tool mutates learned geometry and adds a conceptual caveat about routing context vs. semantic identity. It does not mention side effects, persistence, permissions, or return behavior.

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 two sentences, front-loaded with the action and inputs, and the second sentence is a meaningful caveat rather than filler. It is appropriately sized for the stated purpose.

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 5 parameters including a nested object, no output schema, and no annotations. The description only covers the required inputs and one high-level caveat, leaving optional parameters, input structures, side effects, and return behavior undocumented.

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 compensates only for 'signals' and 'reward' by labeling them as observable/measured. The remaining parameters (domain, weight, cluster_id) are completely unexplained, leaving a significant semantic gap.

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 a specific action ('Update') on a specific resource ('learned task-regime centroid') and names the inputs ('observable signals and measured reward'). It also distinguishes the tool from semantically-oriented siblings by adding 'Learned geometry is routing context, not semantic identity.'

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 context is implied: the tool should be used when you have observable signals and measured reward to update a learned centroid. However, it does not explicitly mention when not to use it or identify sibling alternatives like athena_regime_geometry_resolve.

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