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

Even with readOnlyHint=true, the description adds substantial behavioral context: grounding in tenant data, validation, refusal of unsupported answers, never inventing facts, cost-capped LLM routing, on-box backup, and AEGIS screening. This goes well beyond what the annotations convey and helps the agent trust and handle the tool's responses.

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 core purpose is front-loaded in the first clause, which is good. However, the long parenthetical about Smart Switch Sentinel's routing, cost-capping, on-box backup, and model distillation is marketing-style detail that does not help an agent invoke the tool correctly, making the description more bloated than necessary.

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 read-only QA tool with two self-documenting parameters and no output schema, the description covers the purpose, input scope, and the key behavioral trait (unsupported answers are refused). The exact return structure is not spelled out, but the answer/refusal behavior is transparent enough for an agent to use the tool 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 coverage is 100% and both parameters are already self-described in the schema ('your plain-English question' and 'the tenant to ask about'). The description reinforces the tenant scoping but adds no meaning beyond the schema, so the baseline of 3 applies.

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 states a specific verb ('Ask') and resource ('tenant's FLOCORE operations') with concrete scopes (tickets, queue, state). This clearly differentiates it from the structured siblings like flocore_list_tickets, flocore_queue_summary, and flocore_create_ticket by emphasizing natural-language question-answering grounded in tenant data.

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?

No alternatives are named and no exclusion criteria are given, leaving the agent to infer when to use this tool versus siblings like flocore_recall or flocore_verify_counterparty. The context is clear enough to imply usage — when a plain-English question about tenant operations needs answering — but no explicit routing guidance is provided.

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