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

With no annotations, the description carries the full burden and largely meets it: it discloses the return taxonomy (ACTIVE/WATCH/CLEAR with threshold meaning) and the output organization by predictor group. It does not address data freshness, update cadence, or auth/rate-limit behavior, which would matter for a live data feed.

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?

Five sentences, front-loaded with what the tool is before the methodology and audience detail. Dense but each sentence adds information (methodology, horizon, statuses, grouping, audiences); slightly verbose but no filler.

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?

There is no output schema, so the description must explain the return shape itself, and it does: status values with threshold interpretations, predictor groupings (GDI pillars, commodities/commercial demand), and prediction horizons. An agent can interpret results without additional structured documentation.

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 per the rubric the baseline is 4. There is nothing to clarify beyond what the empty schema already conveys.

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 resource ('statistically validated leading indicator signals evaluated against live supply chain data') and details the underlying methodology (Granger-causal, p<=0.01, >=55% directional accuracy). It is clear what the tool returns, but it does not explicitly distinguish itself from close siblings like get_action_signals or get_signal_narratives.

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 names concrete use contexts (commodity traders for forward-looking positioning, procurement for buy/defer timing, hedge funds for alt-data) and embeds action guidance in the status semantics (ACTIVE = act now, WATCH = prepare). It stops short of stating when NOT to use it or naming an alternative sibling tool.

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