ShowMRR aggregate stats

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

Annotations already declare read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds genuinely new context: the data source (queries showmrr.com) and the data-quality caveat that totals include unverified figures, which materially affects interpretation. It does not mention freshness or rate limits.

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?

Two sentences, both earning their place: the first defines the payload, the second routes usage. The caveat about unverified figures is placed last as a qualifier rather than buried.

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, but the description compensates by naming the returned fields and the caveat on totals. Slightly incomplete on return shape (e.g. how per-category totals are keyed) and on data recency, but sufficient for an agent to call it correctly.

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 the baseline is 4 and there is nothing for the description to disambiguate. No misleading parameter claims are made.

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 (aggregate) and resource (ShowMRR numbers) and enumerates the exact outputs: listing counts, verified count, total listed MRR, per-category totals. This clearly separates it from mrr_leaderboard, mrr_search and mrr_startup, which work on individual entries rather than aggregates.

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?

Gives two concrete trigger conditions: fetching valid category slugs before calling mrr_leaderboard, and gauging dataset size before drawing conclusions. It doesn't state explicit exclusions or say when to prefer a sibling instead, so it stops short of full routing guidance.

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