IBM Granite Embed ($0.005)

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) declare safety and idempotency. The description adds context: uses IBM Granite model with a specified default, returns float arrays per input, and notes no IBM Cloud account required. This is non-contradictory and enhances transparency.

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 two sentences, front-loading the essential purpose and key constraints. Every word adds value; there is no redundancy or filler.

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 simple embedding tool with full schema and annotations, the description covers return type (float array), limits, cost, default model, and use cases. No output schema exists, but the description adequately explains output. Completeness is high.

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 documents both parameters. The description adds the default model string and reiterates limits (1-64 texts, 8000 characters), which is helpful but only marginally extends schema information. 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 uses precise language: 'Generate embedding vectors for one or more texts' and specifies the default model, output format (float array), and use cases (semantic search, RAG retrieval, similarity scoring). It clearly distinguishes from sibling tools like ibm_granite_chat or ibm_granite_code.

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 states the maximum number of texts (64) and the cost model ($0.005 USDC per call via x402), providing practical usage guardrails. It does not explicitly mention when not to use or alternatives, but the sibling names make the distinction clear.

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