ask_m3

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

With no annotations provided, the description carries the behavioral disclosure burden. It does so well by stating the tool is single-turn, independent of Fable, and has specific refusal categories. It also clarifies in-scope domains (neuroscience, cognitive science, AI/ML, CS). Minor gaps remain, such as whether the model has access to conversation history, but the description provides meaningful 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 dense but efficient, packing purpose, scope, exclusions, limitations, and alternatives into a few sentences. Each sentence earns its place, and the most important information—what the tool does—is front-loaded.

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 model-ask tool with three parameters and a fully described schema, the description covers the essential contextual information: the model's identity and scope, refusals, single-turn behavior, how to provide context, and how to choose an alternative tool. Nothing critical is missing for an agent to invoke it 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%, so the baseline is 3. The description adds value by explaining when and how to use the `context` parameter: add a snippet or file path when the question is about existing code. It does not explicitly discuss `context_ref`, but the schema already documents it, so the description's additional guidance lifts this slightly above baseline.

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 identifies the resource (MiniMax-M3 model) and the action (ask it to reason about software/engineering work), listing concrete topics like code structure, data flow, routing, and design trade-offs. It also distinguishes this tool from siblings by stating it is 'independent of Fable' and that `ask` is for Fable and `ask_council` is for both.

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 explicit when-to-use guidance: use this tool for broad, conceptual engineering questions, and add context when asking about existing code. It also states exclusions (offensive security, non-software domains like biology/medicine) and names alternatives: `ask` for Fable and `ask_council` for both.

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