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

Despite annotations covering read-only, open-world, and non-idempotent traits, the description adds substantial behavioral context beyond them: the specific model and dimensions, input limits (32 texts of 8,000 characters), pricing model ($0.002 USDC per call via x402/prepaid), no API key needed, and free trial availability.

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 front-loaded with purpose and key facts, and most sentences earn their place. There is slight redundancy in pricing (title and description both state $0.002, and 'One price per call' precedes the price line), but overall it remains tight and 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?

Given the output schema exists, the description need not explain return values. It sufficiently covers model, dimensions, input format, limits, pricing, and auth requirements, so an agent has everything needed to call the tool 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 100% schema description coverage, the schema already documents both parameters. The description adds useful request-format examples and clarifies the mutual exclusivity of `input` and `text`, plus the per-text character limit and array size, which go beyond the schema's raw types and limits.

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 a specific verb (embeddings) and resource (1024-dimension multilingual vectors via BGE-M3, 100+ languages), and names use cases (semantic search, clustering, RAG). It is immediately distinguishable from sibling tools like chat, summarise, and search.

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 clearly indicates when to use the tool: for semantic search, clustering, and RAG. It does not name alternatives or exclusions, so it is clear context without routing guidance.

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