embed_dataset

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

The description adds meaningful behavioral context beyond the readOnlyHint=false annotation, such as the prerequisite provider and the trade-off with lazy embedding. However, it doesn't disclose potential side effects like resource intensity or what happens to existing cached embeddings, preventing a perfect score.

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 cover purpose, usage context, and requirements. Every sentence earns its place, with no redundant or filler content.

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?

The description is sufficiently complete for a tool with only two parameters and no output schema. It explains what, why, and prerequisites, though it doesn't address what happens at runtime (e.g., cost, duration) or post-conditions, so it's not fully complete.

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 thoroughly documents both parameters. The description adds no additional detail about the parameters themselves, keeping this at the baseline of 3.

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 precomputes column embeddings for semantic search, using a specific verb and resource. It distinguishes itself from alternative lazy embedding in search_data by framing it as an optional warm-up to eliminate latency.

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 explicitly explains when to use this tool versus the alternative (lazily embedding via search_data with semantic=true), and mentions the required embedding provider. This gives the agent clear situational guidance.

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