spraay_compute_embeddings

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

Annotations already indicate non-destructive behavior (destructiveHint=false, readOnlyHint=false). The description adds valuable context: cost ($0.005 USDC) and that it uses 'Spraay Compute', which implies a paid service. This goes beyond what annotations provide.

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?

Two sentences that efficiently convey the core action, use cases, and cost. No unnecessary words; front-loaded with the primary purpose.

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?

For a tool with two parameters and an output schema, the description covers what it does, why it's used, and cost. It could mention the output format (e.g., vector array) but the output schema likely handles that. Overall, it is complete enough for agent selection.

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 coverage is 100%, and the description does not add further meaning beyond what the schema already describes (input string/array, model default). Baseline 3 is appropriate.

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 generates text embeddings and lists use cases (RAG, semantic search, clustering). However, it does not explicitly differentiate from sibling tools like spraay_bittensor_embeddings, which likely serve a similar purpose.

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 mentions typical use cases (RAG, semantic search, clustering), implying when to use it, but provides no guidance on when not to use it or alternatives (e.g., bittensor_embeddings).

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