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

Annotations already establish readOnlyHint=true and openWorldHint=false, so safety is covered. The description goes further by naming the return payload (tok/s, quant, engine, source credit, canonical URL) and asserting 'Never invents numbers', which is a real reliability trait an agent can reason about. It stops short of describing ordering or truncation behavior.

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?

Three short sentences with the purpose and filter set front-loaded, then the return shape. Minimal waste; only the closing 'Never invents numbers' is slightly promotional, though it still carries a trust signal.

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, the description does the necessary work of listing returned fields, and it covers the filter dimensions. Gaps are minor: no explanation of result ranking ('top matches') or of how unpriced builds affect max_price filtering.

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?

Schema description coverage is 100%, so all six parameters are already documented with examples and defaults. The description restates four of the six filters (gpu, model, min_vram_gb, max_price) but adds no syntax or semantics beyond the schema, and omits limit and currency entirely. Baseline 3 applies.

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 and resource ('Search RigBase measured home AI builds') and enumerates the filterable dimensions, so the agent knows exactly what corpus is being queried. It does not, however, differentiate itself from siblings like get_build or best_gpu_for_model.

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 filter list implies the use case (broad lookup over measured builds), but there is no explicit statement of when to choose this over get_build for a known build or best_gpu_for_model for a recommendation. Usage is inferable rather than stated.

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