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Ask a Duck

ask_duck
Read-only

Send prompts to multiple AI providers (OpenAI, Google, Groq, Ollama) with optional images and model selection to debug problems or get diverse perspectives.

Instructions

Ask a question to a specific LLM provider (duck)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe question or prompt to send to the duck
providerNoThe provider name (optional, uses default if not specified)
modelNoSpecific model to use (optional, uses provider default if not specified)
temperatureNoTemperature for response generation (0-2)
imagesNoOptional images to include with the prompt (for vision-capable models)
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the main behavioral traits. Description adds no further details (e.g., response format, rate limits), but does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, no wasted words. However, it is too brief and could include more context without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Description does not specify the output or return format. Given no output schema, the agent needs to infer what the tool returns (e.g., text response). Complexity moderate, but description is incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage for all 5 parameters, so the schema itself provides clear semantics. Description adds minimal value beyond implying the prompt is the question.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool asks a question to a specific LLM provider. It is specific about the resource and action, but does not differentiate from sibling tools like 'chat_with_duck'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., chat_with_duck, compare_ducks). No exclusions or prerequisites mentioned.

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

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