embed

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

With no annotations, the description carries the full burden and does useful work: it declares the prerequisite (an embedding model, with concrete examples), and discloses the return shape ('raw vectors; cosine-similarity them yourself'). It doesn't mention permissions, rate limits, or batch semantics, keeping it below 5.

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?

Three tightly written sentences: the purpose leads, the prerequisite follows, and the output caveat closes. Every sentence carries information an agent needs, with no filler.

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 or annotations, the description supplies the key missing context: model requirement, discovery route via list_models, and the raw-vector return format. Remaining gaps around parameter interplay and input precedence are minor for a 6-param tool with no required fields.

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 67%, so most parameters are already documented in the schema, and the description adds little beyond implying the text/file/model/provider inputs. Naming example models ('nomic-embed-text', 'embeddinggemma') adds minor semantic value for the model parameter, but no guidance on how glob/path/paths interact.

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 and resource ('Embed text/files with an embedding model') and scopes the intent to semantic dedup, clustering, and find-similar work. It distinguishes itself from generation-based siblings and names list_models as the discovery path, so an agent can route without opening the schema.

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?

Provides clear context: use for embedding tasks that 'generation can't do', and instructs the agent to check list_models for embedding-capable models. It lacks an explicit when-not or a named alternative tool for the same task, so it falls short of a 5.

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