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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes beyond these by adding concrete details: no auth or API key, no PII, idempotent and safe to retry. It also discloses rate limits (60 req/min, 250 calls/day), the full response shape including meta fields (available, as_of, source_scope, weighting, units, cache_status, caveats, truncated), and the specific meaning of meta.available=false (data unavailable or cache warming, retry after a few seconds, never report as zero). This significantly enhances behavioral understanding beyond the structured annotations.

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 comprehensive yet efficiently structured. It opens with a one-line summary, then lists explicit use cases, then details the return structure and behavior. Every sentence earns its place: rate limits, meta fields, and error handling all add value without redundancy. The front-loading of the core purpose and use cases ensures an agent immediately understands when to invoke this tool.

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?

Given the tool has no inputs, the description fully covers what an agent needs: what data is returned (data.datasets with specific fields), the shape of the response (data and meta), and how to interpret meta.available=false. It even includes operational details like rate limits and retry behavior. The output schema is marked as present, so the description complements it without over-explaining. Nothing is missing for correct invocation and interpretation.

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 has zero parameters, and the description explicitly states 'No arguments.' The input schema is empty, so schema coverage is trivially 100%. With no parameters, there is nothing to explain, and the description correctly communicates this. It instead focuses on the output and behavior, which is appropriate. No gap exists in parameter semantics.

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 explicitly states the tool's function: 'Coverage, freshness, and build metadata for every mtgmon dataset.' It names the specific resource (mtgmon datasets) and the kind of information returned. It also distinguishes itself from siblings by positioning itself as a meta-tool for dataset status, with use cases like checking data currency and diagnosing meta.available=false, which none of the sibling tools (e.g., get_market_snapshot, get_hmda_product_analytics) cover.

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?

The description provides explicit use cases: 'how current is the data', finding latest available month/HMDA year before quoting figures, and diagnosing another tool's meta.available=false. It also gives behavioral guidance: refresh cadences differ, read each row's coverage and last_updated. While it doesn't explicitly say 'don't use for X', the context is clear enough that an agent can decide when this tool is appropriate versus the other data-specific siblings.

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