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

With no annotations, the description carries the full behavioral burden and does reasonably well: it discloses the return vocabulary (ACTIVE/WATCH/CLEAR per signal), the statistical validation method, and an access restriction ('Paid tier only') that an agent must know before calling. It omits any note on data recency, coverage, or rate limits, which keeps it short of a 5.

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?

Four short sentences, each earning its place: purpose, statistical basis, return values, and access tier. The defining information is front-loaded and nothing is padding.

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?

Since no output schema exists, the description must convey the return shape, and it does by naming the three status values. For a zero-parameter read tool this is close to complete; only data freshness/scope (which GDI universe, how often refreshed) is left unstated.

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 takes zero parameters, so there is nothing for the description to disambiguate; baseline for a no-parameter tool is 4. It correctly avoids inventing or implying inputs that do not exist.

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?

States a specific verb and resource ('Get ... leading indicator signals') and goes further than most by defining what a signal actually is: a Granger-causal relationship at p≤0.01 with a lag and directional accuracy. It does not, however, explicitly distinguish itself from near-neighbors like get_predictive_signals or get_signal_narratives, so an agent must infer the boundary.

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 is only implied by the domain ('leading indicator signals evaluated against live GDI data') and by the hard constraint 'Paid tier only', which usefully gates access. There is no statement of when to prefer this over the similarly named prediction/narrative siblings, so selection guidance is left to inference.

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