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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses that matching is heuristic, keyword-based, offline, deterministic, and that results can be empty or noisy. It also notes the always-ok:true return and fixed nextSteps checklist even on no match, providing rich behavioral context.

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 four sentences, each serving a distinct purpose: purpose, usage, alternative tools, and behavioral caveats. It is front-loaded with the core function and remains concise without redundancy.

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 has a single parameter, an output schema, and rich annotations, the description covers all essential aspects: what it does, when to use it, how it behaves, and what it returns. There are no significant gaps.

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?

The input schema already provides 100% coverage of the single parameter 'symptom' with examples and keyword-matching details. The description adds usage context but no additional parameter syntax or format nuances beyond the schema, so the baseline of 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 matches a plain-language Partner Center problem against documented errors and returns candidates plus an ordered fix path. It explicitly contrasts with sibling tools pc_decode_error and pc_lookup_error, making its specific role unmistakable.

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?

It explicitly says to use this tool when there is no error body and no code, and directs users with raw responses or clean codes to more precise alternatives. This gives clear when-to-use and when-not-to-use guidance.

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