misakanet_preflight

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

With no annotations, the description must carry the burden. It discloses input/output format ('Input: intent, context', 'Output: risk level, matched lessons, guards') and implies read-only behavior ('Check risk level', 'Provide proactive warnings'). No contradictions, and it adds valuable behavioral context beyond the schema.

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 front-loaded with purpose. It uses clear labels for usage, input, and output, with no wasted words. Every sentence earns its place.

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 2 params and no output schema, the description covers purpose, usage, and input/output adequately. It doesn't mention error cases or side effects, but given the tool's low complexity and good annotations coverage, it's sufficiently complete.

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 coverage is 100% with descriptions for both intent and context. The description restates the parameters and gives examples, but adds minimal new meaning beyond the schema itself, so baseline 3 is appropriate.

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 clearly states the tool checks risk level before high-risk operations, matching agent intent against lesson triggers. It uses a specific verb 'check' and resource 'risk level', and the purpose is distinct from sibling tools like search or get_lesson.

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 explicitly says to use before RAG builds, WSL/GPU tasks, bulk imports, or any operation that might fail, providing clear context. It doesn't exclude when not to use, but the examples and general rule give sufficient guidance.

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