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

The description adds valuable behavioral context beyond annotations: it calls no model, costs nothing, never runs out of quota, and is deterministic. This complements the readOnly and idempotent hints and helps agents understand side-effects (none), cost implications, and reliability.

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 with a distinct purpose: core function, recipe capability, determinism/cost, and usage guidance. It is front-loaded with the primary behavior and has no fluff or 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?

The description covers what the tool does, when to use it, key behavioral traits, and parameter semantics. Combined with the rich annotations and output schema, this gives an agent all necessary context to select and invoke 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?

The schema already documents the single 'task' parameter with examples. The description adds the crucial detail that the task can be in 'any language' and provides more example domains (invoices, bank statements, CSV). This enhances understanding of what the parameter accepts.

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's specific function: it takes a plain-language task and returns which server tools can do it, including example calls and multi-step recipes. This distinguishes it from sibling tools like list_models or ask_model, which either list tools or answer questions.

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 'Call this FIRST when you are not sure what this server offers,' giving a clear when-to-use. It also says 'instead of reading the whole catalogue and guessing,' which is a when-not-to-use, but it does not name a specific alternative tool like ask_model. The guidance is strong but not exhaustive.

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