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

Annotations only declare readOnlyHint=true, but the description goes well beyond that by disclosing the sum-of-votes rule, the compression exception that withholds the Trend axis vote, the informational non-voting asymmetry lean, the explicit thresholds, and the deterministic behavior. This is far richer than the annotations alone and gives the agent a clear model of the tool's 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 dense but every sentence earns its place: the core classification is front-loaded, and the vote rule, exception, lean, thresholds, determinism, and usage positioning are all operationally relevant. The detail is justified by the complexity of the regime logic.

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?

With an output schema already covering the return shape and zero input parameters, the description supplies exactly the contextual information an agent needs: the composite nature, the exception behavior, the asymmetry lean, and when to use the tool. Nothing essential is missing.

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?

The tool has zero parameters, so there is no parameter ambiguity and the empty input schema is fully self-contained. The description uses this opportunity to clarify output semantics and behavioral rules, which is more valuable than restating empty parameter details.

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 defines the tool as a deterministic market-regime classifier across seven explicit axes and states exactly what it produces: per-axis state, supporting numbers, votes, and an overall posture. It effectively distinguishes itself from raw-data sibling tools by positioning it as a composite first-call regime read.

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 explicitly frames the tool as a first call and explains that funding, sentiment, and flows should be interpreted differently depending on this regime. This gives an agent strong context for when to invoke it, though it does not list explicit when-not-to-use cases or name specific sibling tools as alternatives.

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