ask_ollama

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

With no annotations, the description carries the full behavioral burden. It discloses several non-obvious traits: operation is single-turn, reached via a local signed-in ollama daemon with no API key, model default behavior, and refusal categories. This goes well beyond the schema, though it stops short of describing output format or side effects, so a 4 is appropriate.

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 lengthy but each clause earns its place: scope, model selection, transport, turn semantics, acceptable topics, refusals, and an alternative. It is front-loaded with the primary purpose. Slight verbosity in the refusal list prevents a 5, but the structure is logical and efficient.

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 Q&A tool with no output schema, the description covers nearly everything an agent needs: allowed questions, refused categories, context parameter usage, model selection, and the council alternative. It doesn't describe the response shape or error behavior, but those are not critical for invoking a single-turn question tool. The large sibling set is handled by explicit differentiation from ask_council.

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 description coverage is 100%, so the baseline is 3. The description adds real value by giving concrete model id examples, explaining when to use `context` ('add a snippet or file path... when the question is about existing code'), and clarifying the default behavior when `model` is omitted. This lifts it above the 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 opens with a specific verb and resource: 'Ask a single Ollama Cloud model — on its own — to reason about the SOFTWARE/ENGINEERING work.' It clearly enumerates the topics (code structure, data/control flow, architecture, design trade-offs) and contrasts with ask_council, so an agent can distinguish it from siblings without inspecting the schema.

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 provides explicit when-to-use guidance: broad conceptual engineering questions are fine, while offensive-security and non-software domain questions are refused. It also tells the agent to add context for existing-code questions and points to an alternative tool ('Use ask_council to mix Ollama models with Fable'), covering both positive and negative cases.

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