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

Beyond annotations (readOnly/idempotent/non-destructive/closed-world), the description discloses statelessness, a 32 KB payload cap, a 60 RPM rate limit, demo mode, a 10 sats x402 cost, and HTTP 400 on invalid input. These are operational constraints an agent cannot get from the annotation block.

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?

Front-loaded with the core verb and settlement mechanism, then routes to the alternative, then lists limits. Dense but every clause carries information; slightly crammed parenthetical packing keeps it just under a 5.

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 2-param read-only embedding tool with an output schema (so return values need not be explained), the description covers payment model, limits, error behavior, and default serialization. Nothing required to call it correctly is missing.

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% so the baseline is 3, but the description adds value: it restates the 'encoding_format' default ('float_array') and introduces a '?demo=true' query switch not present in the schema. It does not describe vector dimensionality or text size expectations beyond the 32 KB cap.

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?

States a specific verb+resource ('Generates normalized high-dimensional semantic vector embeddings') with its settlement mechanism (Solana SPL USDC x402), and explicitly distinguishes itself from the sibling extract_structured_json_entities. An agent can tell exactly what this does and what it is not.

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?

Explicit selection guidance: 'Use when computing text embeddings paid via Solana USDC x402 signatures rather than Bitcoin Lightning', plus an explicit alternative for the adjacent task ('for extracting discrete JSON entities ... use extract_structured_json_entities instead'). Both inclusion and exclusion conditions are named.

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