qc_learnings

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

With no annotations, the description carries the load and does well: 'hard-coded' signals a static read-only library, and it discloses that no arguments returns the index. It doesn't state permissions or size/rate behavior, but for a local lookup these are minor.

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 sentences, front-loaded with what the library holds, then when to call, then how parameters behave. No filler.

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 zero-required-param, no-output-schema lookup tool, it covers what the tool contains, when to use it, and the index-vs-filtered return distinction. Nothing essential is missing.

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% so the schema already documents id, tag, and query. The description adds the semantic that omitting all params yields the index rather than an error, which is genuine value beyond the schema.

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 resource ('hard-coded QC learnings library') and its content (failure patterns with recipes), distinguishing it from siblings like qc_run_checklist and qc_check_api which act on tests rather than serve knowledge.

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?

'Query BEFORE debugging a red CI, a suspicious green suite, or a deploy mismatch' gives concrete triggering contexts. It stops short of explicit exclusions or naming an alternative, but the when-to-use is unambiguous.

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