consult_ollama

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

No annotations are provided, so the description carries the full burden. It discloses support for sequential chaining and model selection logic, but lacks information on side effects, idempotency, authentication requirements, or rate limits. The behavioral traits are partially covered.

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 concise at two sentences, covering purpose and a key feature (chaining). It is front-loaded with the primary use. However, it lacks structure such as bullet points or explicit sections that could improve scannability.

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?

Given the complexity (8 parameters, nested objects, no output schema), the description offers adequate context about usage and chaining. However, it does not describe return values, error conditions, or the typical response format, leaving a completeness gap.

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 minimal value beyond the schema, only tying together the chaining context for the 'context.previous_results' parameter. Most parameter meaning is already clear from the schema itself.

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 specifies the tool's purpose: consulting with Ollama AI models for architectural decisions, code reviews, and design discussions. It includes mention of sequential chaining, which helps differentiate from the sibling tool 'list_ollama_models' but could be confused with 'sequential_consultation_chain'.

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 implies usage for reasoning tasks but does not explicitly state when to use this tool versus alternatives like 'compare_ollama_responses' or 'sequential_consultation_chain'. No exclusions or when-not-to-use guidance is provided.

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