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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: fallback from knowledge base to human specialist, ticket number format (letter+2 digits), async draft preparation, and email notification to specialist. It also mentions filtering against off-target requests, injections, and profanity. This is meaningful behavioral context beyond what a typical tool description would include.

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 single paragraph but information-dense, covering purpose, pool types, flow, topics, and security filters. It is not excessively long, and every sentence contributes meaningful detail. However, it lacks visual structure (bullets or sections), making it slightly harder to scan quickly. Still, it earns its place overall.

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 tool with 8 parameters, 0 required, and no output schema, the description provides a solid overview of the consultation process, fallback behavior, and topic scope. It does not explain return values or error handling, but the flow description gives enough context for an agent to understand expected behavior. The mode semantics are left to the schema, which is adequate. Slight gap: no explicit guidance on when each mode is appropriate, but this is partially covered by the schema descriptions.

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 has 100% coverage with descriptions for all 8 parameters, so the baseline is 3. The tool description adds value by specifying the exact ticket number format (letter+2 digits, e.g., А05) and elaborating on pool types and modes within the flow context. This extra information helps clarify how parameters relate to the overall process.

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 it provides service consultation for pool maintenance across multiple pool types and lists specific topics. The verb 'consult' and resource 'pool maintenance' make the purpose explicit. However, it does not explicitly differentiate from sibling tools like 'tech_consult', relying instead on topic enumeration to imply scope.

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 a clear flow (knowledge base → miss → ticket with number → AI draft → specialist email) and lists covered topics, which implies when to use the tool. It also mentions protection against off-target requests, suggesting the tool is for pool-related inquiries only. However, it does not explicitly state alternatives or conditions for using sibling tools, so usage guidance remains implicit rather than explicit.

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