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

Annotations only declare readOnlyHint=true and openWorldHint=false; the description goes further by enumerating the actual content returned (principles, field meanings, verdicts, per-market/per-timeframe coverage), which is the behavior an agent needs to know. It does not state the return format (text vs. structured), a minor gap for a zero-parameter reference tool.

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?

A single front-loaded sentence with a colon-delimited inventory of contents; nothing is redundant and the scope is stated before any detail.

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 output schema and no parameters, the description carries the burden of telling the agent what comes back, and it does so by listing the four content areas. Only the response format/size is left unspecified, which is a small residual gap for a documentation tool.

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 no parameters, so the baseline is 4. The description correctly implies the full methodology and coverage matrix is returned unconditionally with no filtering options.

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 names the resource precisely: documentation of how the statistics are computed, including principles, field/verdict meanings, and data coverage. That is clearly distinct from the analytical siblings (compare_patterns, get_pattern_stats, search_patterns), though it never explicitly states the contrast.

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: an agent infers it should call this to interpret fields and verdicts returned by the stats tools, or to check coverage per market/timeframe. There is no explicit 'call this before/after X' statement and no naming of alternatives to avoid.

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