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

Annotations already declare read-only/idempotent/non-destructive, so the bar is lower, yet the description adds real behavioral context: odds are suppressed until 60 observed days while counts remain visible, absence is explicitly not precision, and frequencies are not calibrated forecasts. These are exactly the caveats an agent needs to interpret the output correctly.

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, front-loaded with what is returned before the caveats. Some phrasing ('absence is never precision') is cryptic jargon rather than plain disclosure, which slightly reduces clarity per word.

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 no parameters and no output schema, the description carries the interpretive burden and does so: it says what is returned, when values are withheld, and how to treat them. The only gap is the undefined notion of a 'layer' and the output shape, which a caller must discover by invoking.

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, and the description does not invent any. Baseline for a parameterless tool applies, with no schema semantics left unexplained.

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 ('Read') and resource ('counted Markov transitions and empirical 5/20-day reach odds'), so the agent knows exactly what data comes back. It does not differentiate itself from the many sibling board/evidence tools or explain what a 'layer' means, which keeps it below a 5.

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?

There is no explicit when-to-use vs sibling-tool guidance, but 'Display-only, never a state driver' does constrain how the output may be used. Usage is implied rather than routed to an alternative.

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