embeddings_api_v1_embeddings_post

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

No annotations provided, so the description must convey behavior. It mentions performing user/model checks and dispatching to backends, and returns an OpenAI-compatible response. However, it does not disclose side effects, auth requirements, rate limits, or error handling. With no annotations, this is a moderate disclosure.

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 structured with clear Args and Returns sections. It is informative but not overly verbose; the list of supported providers adds value. Minor redundancy in 'This handler:' line, but overall efficient for a technical endpoint.

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 simplicity (1 parameter, no output schema, no annotations), the description covers purpose, input format, and response type (OpenAI-compatible). It does not include response structure details or error cases, but the OpenAI-compatible indication is well-recognized. Slightly incomplete for agents needing exact field names.

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 input schema has only one required parameter (form_data) with no description and 0% coverage. The description adds crucial meaning by explaining that form_data is an OpenAI-like payload containing model and input fields, which goes beyond the schema's bare object type.

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 explicitly states 'OpenAI-compatible embeddings endpoint', which clearly identifies the tool's purpose: generating embeddings. It further elaborates on dispatching to backends and supporting multiple providers, distinguishing it from siblings like get_embeddings (likely for retrieval) and reindex endpoints.

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 implies use for generating embeddings with an OpenAI-compatible payload, but does not explicitly state when to use this tool versus alternative embedding endpoints (e.g., get_embeddings or reindex). It lacks exclusion criteria or alternative references.

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