embed_text

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

With no annotations, the description carries the burden of disclosing behavior. It explains the truncation dimension with allowed values, normalization behavior, and the return format (dict with embeddings and dimension). It does not mention any side effects or additional context, but for a pure embedding function this is adequate.

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 clean docstring: one-sentence summary, argument list, return statement. No filler words or redundant information.

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 3-parameter tool with no output schema or annotations, the description covers all parameters and return value. It lacks explicit use-case context but provides enough for correct invocation. It could be improved by stating that it is a pure read-only operation, but that's implied.

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 description coverage is 0%, so the description must fully compensate. It does: it explains texts as one or more strings, dim with specific allowed values (512, 256, 128, 64, 32), and normalize with L2 normalization and a recommendation. This goes well beyond the schema's bare property definitions.

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 'Embed text(s) into vectors,' which is a specific verb+resource statement. It clearly differentiates from sibling tools like search and similarity by describing the core embedding function.

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 explicit guidance on when to use embed_text versus sibling tools like similarity or search. It does provide parameter-level guidance (normalize recommended for cosine similarity), which implies a usage context but doesn't address tool selection.

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