Generate text embeddings without storing them in a vector index. Use for dimension checks, model comparisons, and dry-run vector previews.
Generate embeddings without storing them - useful for dimension checks.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | dense | |
| model | No | ||
| texts | Yes | ||
| provider | No | ||
| dimension | No | ||
| input_type | No | passage |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full behavioral burden. It does disclose the key behavioral trait — embeddings are not persisted — but omits provider/model selection behavior, default model implications, rate limits, or whether the call is free. This is significant for a 6-param call with provider/model choices.
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?
Single front-loaded sentence with zero filler. Efficient and immediately scannable.
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-value explanation isn't needed. But with 0% schema description coverage, 6 parameters, and no annotations, the description is too thin to guide correct invocation — an agent can't tell how provider/model/dimension interact or what defaults produce.
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 0% and there are 6 parameters, including provider/model/dimension/kind/input_type enums that critically affect the embedding produced. The description adds no parameter meaning at all — not even which provider defaults apply or what 'dimension' overrides.
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 clear verb+resource ('Generate embeddings') and adds a distinctive scope qualifier ('without storing them') that separates it from incidental embedding during upsert/query. However, it doesn't name a sibling alternative (e.g., upsert_vectors or query_vectors) to fully disambiguate.
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?
Only a hint of when it's useful ('dimension checks'). No explicit when-to-use vs alternatives (e.g., use query_vectors when you also need search results, or upsert_vectors when you need persistence). An agent has to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.