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 is free and requires no key, and describes the conceptual output (regime events with conviction and duration). It does not mention rate limits or edge cases, but the output schema fills in return structure.

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 four sentences, each serving a purpose: an engaging hook, the return details, usage timing, and access note. It is front-loaded with the core purpose and wastes no words.

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 parameterless tool with an output schema, the description provides complete context: what the regime represents, how to use it in a workflow, and access requirements. The output schema covers the specific return fields, so no additional detail is needed.

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 the baseline is 4 per the rubric. The description appropriately focuses on output semantics rather than parameter details, which are nonexistent.

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 returns the current market regime (accumulation, distribution, trending, reorganizing) with conviction and duration, as latest_event plus all active_events. This distinguishes it from sibling tools like get_directional_bias or get_convergence, which focus on individual metrics.

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?

It explicitly advises using this tool to pick the playbook (trend-following vs mean-reversion vs sit out) BEFORE interpreting any individual reading, providing clear contextual guidance. However, it does not name specific alternative tools for contrast, so it misses the top score.

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