embed_text

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions the output dimension (768) and the model, but does not explain the return format, input size limits, batch handling, or any side effects. This lack of detail leaves the agent uncertain about the tool's runtime behavior.

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 a single, efficient sentence that front-loads the main verb 'Generates' and immediately conveys the purpose, model, and dimensions. Every word earns its place; there is no filler or repetition. This is exemplary conciseness.

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?

Given the tool has one parameter, no annotations, and no output schema, the description should provide more context about return values and constraints. It only states the embedding dimension and use case, but omits practical details like output format (e.g., list of floats), maximum input length, and batching limitations. An agent would need to infer or experiment to use this tool reliably.

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 schema description coverage is 0%, so the description must compensate for explaining parameters. The description does not mention 'text' at all, nor does it clarify accepted input types (string vs array) beyond what the schema already states. It adds no semantic value for the parameter, failing to help the agent understand how to construct inputs effectively.

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: generating 768-dimensional dense vector embeddings for RAG and semantic search. It names a specific model (BAAI BGE-Base), adding specificity. However, it does not explicitly distinguish itself from the sibling tool 'embed_multilingual', which likely also generates embeddings, so it falls short of a 5.

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 provides no guidance on when to use this tool versus alternatives. It mentions 'RAG & semantic search' as use cases, but does not explain when to choose embed_text over embed_multilingual or other sibling tools. There is no statement of exclusions or preferred scenarios, leaving the agent without clear selection criteria.

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