ask_deepseek

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

With no annotations provided, the description carries the full disclosure burden and largely succeeds: it reveals the single-turn nature, the ASK_FABLE_DEEPSEEK_API_KEY prerequisite with its not_configured failure mode, refusal categories, and in-scope domains. It stops short of describing the response payload on success or refusal, but the key behavioral gotchas are disclosed.

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?

Though longer than the calibration ideal, every sentence earns its place: model identity, scope, cost preference, config prerequisite, refusal policy, and sibling routing. It is front-loaded with the core purpose and contains no filler or repetition of schema content.

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 3-parameter, no-output-schema, no-annotation tool, this description is nearly complete: what to ask, how to pass context, config requirements, refusals, and routing to siblings. The only minor gap is expected response behavior on refusal or misconfiguration, but for a simple ask tool the primary answer shape is self-evident.

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, and the description adds genuine value by clarifying model identity for the question parameter and instructing when to populate context ('add a snippet or file path in context when the question is about existing code'). context_ref is fully served by the schema, so no further elaboration is needed.

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+resource: 'Ask the DeepSeek model (deepseek-v4-pro...)' and a clear engineering scope — code structure, flow, architecture, design trade-offs. It explicitly distinguishes itself from Fable and names the model, making it unmistakable against the large ask_* sibling family.

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?

Provides explicit when-to-use guidance ('Cheap direct API — prefer it... for a quick independent opinion'), when-not-to-use conditions (offensive-security and non-software domains are refused), and named alternatives ('Use ask for Fable, ask_m3 for MiniMax, ask_glm for GLM, or ask_council...'). Nothing is left to inference.

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