local_embed

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

With no annotations, the description carries the behavioral disclosure burden. It states the core behavior and the Ollama preset for the model, but it does not mention input limits, handling of empty/large texts, or error behavior. Core behavior is clear, but some operational details are absent.

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 compact and front-loaded: purpose first, then use cases, then argument details. Every sentence adds useful information, and the Args section is minimal and scannable.

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 relatively simple embedding tool, the description covers what the tool does, when to use it, and both arguments. The output schema exists, so return values do not need to be documented here. It does not explicitly say the computation is local, but the tool name and 'Ollama preset' strongly imply 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 has no property descriptions, so the description must compensate. It explains that 'texts' are the texts to embed and that 'model' is the embedding model, notably adding the non-obvious Ollama preset context beyond the schema default. It could specify allowed model choices, but it covers both parameters adequately for a simple two-argument tool.

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: 'Computes embedding vectors for a list of texts.' It also distinguishes the tool from likely language-model siblings by noting it works 'without troubling a large language model,' so an agent can tell local_embed apart from local_ask and local_structured.

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?

It gives clear use cases: similarity comparisons, duplicate detection, and rough topic sorting. It also implies when not to use a generative LLM, which is helpful for choosing between this tool and sibling tools, though it does not explicitly name alternatives or exclusions.

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