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

The description adds valuable behavioral context beyond the annotations: it discloses that the data is 'live aggregate statistics,' explains the distinction between total_principles and full corpus size on Pro/Enterprise, and mentions the last-validated timestamp. This gives the agent insight into what the results represent and when they might be incomplete.

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 compact and well-organized: it leads with the core purpose, then clearly labels INPUT, RETURNS, and USE WHEN sections. Every sentence adds necessary information without redundancy or fluff.

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?

For a simple tool with no parameters and rich annotations, the description covers the essential aspects: what it returns, when to use it, and the availability caveat for Pro/Enterprise. It is fully sufficient for an agent to invoke it correctly and interpret the results.

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?

With zero parameters, the schema already fully covers the absence of inputs, and the description confirms 'INPUT: none.' No additional parameter explanation is needed, matching the baseline of 4 for no-parameter tools.

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 opens with a specific verb-resource pair: 'Return live aggregate statistics for the Proximens GEO Engine knowledge base.' This clearly distinguishes it from sibling tools like 'audit_url' or 'search_principles' which perform different actions.

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 includes an explicit 'USE WHEN you need to gauge the size and quality of the corpus before relying on it,' providing a clear use case. However, it does not name any alternatives or specify when not to use this tool, so it stops short of the highest level of guidance.

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