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

No annotations are provided, so the description carries the burden and largely meets it: it discloses defaults (turbo), native dimensions per model, the truncation behavior and that truncation is Matryoshka re-normalized, and the query-prefix behavior of input_type. It does not cover auth, rate limits, batch size limits for array input, or vector normalization of untruncated output, leaving some behavioral gaps.

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?

Information-dense and front-loaded: purpose first, then routing guidance, then model selection, then the optional truncation. No filler sentences, though the model-tier list duplicates schema content and could be trimmed.

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?

An output schema exists, so return values need not be explained, and the description covers model choice, input_type routing, and dim truncation adequately. Missing only edge-case behavior such as array/batch limits or error conditions for a tool with array input.

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 100%, so the schema already documents all four parameters, including model tiers and dim truncation. The description's restatement of model dimensions and the quality/cost ordering adds light reinforcement but not new semantics beyond the schema, so baseline 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?

States a specific verb+resource ('Generate vector embeddings for one or more texts with Forge') and immediately differentiates from the only sibling (list_models) by describing the actual generation operation. The downstream use cases (semantic search, RAG, clustering, similarity) make the intent unambiguous.

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?

Gives clear context for when to use the tool and, importantly, when to use each input_type ('query' for search queries, 'document' for content you index) and how to pick a model by quality/cost tradeoff. It lacks explicit when-not-to-use or alternative-tool routing, but with only one sibling that is a minor gap.

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