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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description goes further by disclosing cost ($0.01/call), the free tier (first 25), that failed calls are free, and that repeats are billed — directly explaining the otherwise puzzling idempotentHint=false. It also states explicitly that nothing is modified, matching the annotations rather than contradicting them.

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?

Output spec is front-loaded, followed by use cases and then pricing/commercial terms, with no filler sentences. The pricing clause is dense with numbers but every element (price, free allowance, failure policy) affects whether an agent should call it.

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?

There is no output schema, and the description fully specifies the return artifact (1,024-dim vector), input limit, model, multilingual scope, cost model, and side-effect profile. An agent has everything needed to decide and to call 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?

With one parameter and 100% schema coverage, the schema already documents the field and its 4,000-character limit, so the baseline is 3. The description adds value with a worked example showing the parameter takes raw content to embed (not an instruction or query), which disambiguates how to populate it.

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 states exactly what is produced (a 1,024-dimension vector from BAAI BGE-M3, up to 4,000 characters, 100+ languages), which is specific enough to distinguish it from sibling generation tools like ai_chat or ai_summarize_text. It conveys the resource and output shape rather than restating the name, though the operative verb ('embed') is left implicit.

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?

It gives concrete intended uses (semantic search, RAG retrieval, clustering, deduplication), which tells an agent when this tool is appropriate rather than ai_rerank or ai_classify. It stops short of explicit exclusions or naming the nearest alternative for near-duplicate tasks.

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