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

No annotations are provided, so the description carries the full burden. It clearly discloses that derived.tracker_density is a weighted blend, not a measurement, and explains why data_cost_usd is excluded. This adds meaningful behavioral context beyond the raw schema.

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 dense technical paragraph, but each sentence adds distinct value: output structure, component facts, derived score semantics, and use case. It could be more front-loaded, but it is not bloated.

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 tool with a single parameter and no output schema, the description covers the return value meaning (facts and derived score) and the purpose (anchoring an adversary class). It falls short of providing the exact output structure but is sufficiently informative.

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 input schema already fully documents entity_slug with format details, and the description adds no parameter-specific information. Baseline of 3 is appropriate given high schema coverage.

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 explains the output structure (component counts and derived.tracker_density) but lacks an explicit verb or action, so an agent can infer it returns density data for an entity but not precisely what operation it performs. It differentiates the tool's unique derived score concept but not the primary purpose.

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 on when to use this tool vs alternative signal tools (e.g., signal_halo_score, signal_dark_pool_risk). The mention of anchoring the cross-lens classifier is a downstream use case, not an agent-facing usage guideline.

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