get_local_embeddings

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the core function and omits important details such as output format, model selection behavior, blocking behavior, or requirements like having a model loaded.

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?

A single, grammatically complete sentence that expresses the core purpose without redundancy. It is well-structured and front-loaded, earning a high conciseness score.

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?

No output schema is provided, and the description does not specify what the returned vector representation looks like (e.g., array of floats, dimensionality, model used). It also omits operational context like model loading, making the tool usage ambiguous for agents.

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%, and the schema already describes the input and model parameters. The description adds no additional parameter context, so the baseline score of 3 applies.

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 uses a specific verb ('generate') and resource ('vector representations of text'), clearly indicating embedding generation. It distinguishes from sibling tools such as query_local_llm or analyze_local_image, though it does not explicitly name alternatives or scope constraints.

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?

No guidance is given on when to use this tool versus alternatives like query_local_llm or search_local_docs. The description does not mention prerequisites, use cases, or exclusions, leaving the agent to infer from the tool name.

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