ollama_generate_text

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

With no annotations, the description carries the full burden for behavioral disclosure. It discloses that the tool runs locally, is token-efficient, and is not suited for complex reasoning. However, it lacks details on execution behavior such as streaming, latency, error handling, or output format, which would be valuable for an agent.

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 only two sentences, front-loaded with the primary purpose, and each sentence delivers distinct, useful information without redundancy. It is appropriately sized and easy to parse.

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 simple generation tool with no output schema or annotations, the description covers the core purpose, use cases, and limitations. It could mention the expected return value or explicitly differentiate from ollama_chat, but overall it provides sufficient context for an agent to decide when and how to invoke it.

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?

The input schema provides 100% coverage with descriptions for all four parameters (prompt, model, max_tokens, temperature). The description adds no parameter-specific meaning beyond what the schema already offers, so the baseline score of 3 is appropriate.

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 states the tool generates text using local Ollama, specifies the scope (SIMPLE, token-efficient tasks) and provides concrete examples (basic content, error messages, placeholder text, boilerplate code, routine documentation). This specificity differentiates it from broader generation tools and implies its place among the sibling tools.

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 ('Use instead of Claude for non-analytical text generation') and strong when-not-to-use guidance ('AVOID for complex reasoning, analysis, or creative writing...'). However, it does not explicitly reference sibling tools like ollama_chat or ollama_code_generation as alternatives, leaving some ambiguity within the Ollama tool family.

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