zerodb_embed_text

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

No annotations are provided, so the description must convey behavioral traits. It only states what it does and a general use case, but does not disclose output format, safety/read-only nature, rate limits, or any dependencies (e.g., model download). The lack of output schema makes this gap more significant.

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 two sentences, front-loaded with a clear verb ('Generate'), and includes only essential information. No wasted words or 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?

With no output schema and no annotations, the description should explain return values and integration context. It implies the output is an embedding vector but gives no detail on return format or how the vector can be used. It mentions custom similarity calculations but does not reference sibling tools. Moderate completeness for a simple tool.

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 descriptions for all three parameters, including model dimensions and default normalization. The description adds no parameter-specific detail beyond what the schema already provides, 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 'Generate vector embeddings for text' with a specific verb and resource. It also names the model family (BAAI BGE) and differentiates from siblings by positioning it for 'manual vector operations or custom similarity calculations,' making it distinct from higher-level tools like zerodb_semantic_search.

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 provides a clear use case: 'Useful for manual vector operations or custom similarity calculations,' which serves as guidance for when to use this tool. However, it does not explicitly name alternative tools or state when not to use it. The context is sufficient but not exhaustive.

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