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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explicitly discloses return semantics (candidates, verdicts pass/fail/unknown, evidence basis observed vs inferred, explicit no-hit semantics), output limits (12 default, 20 max), and telemetry behavior (aggregate stores zero user text; debug stores need for 30 days). It omits some specifics like rate limits or failure modes, but for a read-only advisory tool this is strong transparency.

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 a dense, information-rich paragraph with good forward structure: purpose first, then usage trigger, then input, then output semantics. Every sentence earns its place. It could be slightly more scannable with bullet points, but it is appropriately sized for the complexity of the tool and adequately front-loaded with the core decision-tool purpose.

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's complexity (7 params, nested objects, output schema present), the description is remarkably complete. It covers when to use, what to send, return semantics, verdict model, evidence basis, no-hit behavior, telemetry implications, and the license-matching nuances handled within the schema. The output schema exists so return-value explanation is not needed. This is fully adequate for an agent to invoke 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?

Schema description coverage is 71%, which is high. The description adds meaningful context beyond the schema: it explains the constraint verdict model (pass/fail/unknown) that maps to the constraints object, describes the evidence basis, and names the excluded-from-exclusion semantics. The telemetry parameter's enum behavior is explained (aggregate vs debug). The description compensates well for the remaining undocumented params.

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 is specific and action-oriented: 'Pre-adoption decision tool for open-source reuse. Call BEFORE implementing non-trivial functionality.' It states the exact trigger conditions and the return semantics (candidates count, per-constraint verdicts, evidence basis, no-hit semantics). It clearly distinguishes from sibling search_components by framing itself as a decision/pre-adoption tool rather than a generic search.

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 gives explicit when-to-use guidance ('Call BEFORE implementing non-trivial functionality... or before adding a dependency') and what to send ('a short capability need plus your project's minimal constraints'). It includes exclusions (no project names, paths, or secrets). The sibling search_components exists, and this description positions this tool as the pre-adoption decision tool, providing clear usage context.

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