ingest_vectors

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

With no annotations, the description carries the full burden. It discloses that records are queued for indexing (async behavior) and mentions optional metadata. It could include more on failure handling or rate limits, but the given info is sufficient for basic understanding.

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 concise sentences front-loaded with purpose, then usage details. Every sentence adds value without redundancy. Length is appropriate.

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?

Despite no output schema, the description covers ingestion behavior (upsert, queuing, status polling) and addresses edge cases (large dumps). It is self-contained for an agent to use correctly.

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 is empty with additionalProperties allowed, so the description fills the gap by specifying that each record requires 'id', 'values array', and 'optional metadata'. This adds critical meaning beyond the schema.

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 clearly states the tool action 'Ingest (upsert) vector records into a dataset' with specific verb and resource. It distinguishes from sibling tools like create_dataset (creates dataset) and query_vectors (searches) by focusing on record ingestion.

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 explains to poll get_dataset for indexing status and advises using an async import flow for large dumps >10 MiB, providing practical guidance. However, it does not explicitly name the alternative async tool, slightly reducing clarity.

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