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

The description discloses a cost side effect not present in annotations: 'Settles $0.003 USDC; no charge on failure.' It also adds hard constraints (Prompt <=8000 chars, maxTokens <=1024) beyond what annotations provide. Since readOnlyHint=false and idempotentHint=false, this cost disclosure adds meaningful behavioral context that annotations alone do not convey.

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 four sentences but packs purpose, usage, constraints, exclusions, and cost into a compact block. Every sentence contributes new information, though the title partially duplicates the same framing, making the overall title somewhat verbose.

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 an output schema and fully documented parameters, the description covers the critical gaps: cost, when to use, what not to use, and token limits. An agent has enough context to decide whether and how to call the tool without opening the schema, and the output schema covers return structure.

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. The description restates constraints already present in the schema (e.g., 'json:true forces a JSON object' mirrors the schema's 'Force a JSON object response'; token and char limits match schema boundaries). It does not add new parameter semantics beyond reconfirming behavior already documented structurally.

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 and resource: 'Cheap small-model text completion for agent sub-tasks: classify, extract, summarize, rewrite.' This clearly distinguishes it from siblings like compute_encode, compute_hash, or compute_search, which are not text-completion tools. The title reinforces the input/output contract ('prompt in, text or JSON object out').

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 explicitly says 'MUST be invoked when an agent needs a short LLM answer without paying flagship rates' and 'Do NOT use for long-form generation or tool calling.' This gives both when and when-not conditions, leaving no ambiguity about the tool's intended scope.

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