ollama_embed_text

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

No annotations are provided, so the description carries the burden. It discloses that the tool uses local models and saves Claude tokens, which are useful behavioral traits. However, it does not mention prerequisites like model availability, return format, or rate limits. This is moderate disclosure, neither bare nor comprehensive.

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 three sentences, each providing distinct value: what it does, use cases, and when to use it. It is concise, front-loaded with the primary action, and avoids redundancy.

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 two-parameter tool with no output schema, the description covers the purpose, use cases, and a benefit. It implicitly conveys the return type (embeddings) through the name and context. Minor gap: no mention of model management prerequisites, but sibling tools like ollama_list_models and ollama_pull_model suggest that.

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%, with both 'text' and 'model' parameters clearly described in the schema. The description adds no additional parameter details beyond mentioning the default model in an example, which the schema already covers. Baseline 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's function: generate text embeddings using local models. It distinguishes from siblings like ollama_generate_text and ollama_chat by specifying embedding generation and provides concrete use cases (semantic search, similarity, clustering).

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 gives explicit context on when to use the tool: ideal for batch embedding tasks, semantic search, similarity comparisons, and clustering, and for routine embedding generation to save tokens. It lacks explicit alternatives or exclusions, but the usage context is clear and helpful.

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