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

No annotations are provided, so the description carries the full burden. It adds useful context like 'verified 2026 provider prices' and the cost components, but doesn't disclose the return format, assumptions (e.g., chunking strategy), or excluded costs (e.g., ingestion).

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?

It is a single, front-loaded sentence with no redundant phrasing; the em-dash separates the title from the core purpose efficiently. Every word earns its place.

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 tool's complexity (9 required parameters, no output schema, no parameter descriptions), the description is too sparse. It lacks information about what the output looks like, how inputs map to the cost components, and any assumptions or limitations. A richer description is needed.

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 0% and the description provides no parameter-specific explanations. Parameter names like 'chunkTokens' and 'retrievedTokensPerQuery' are ambiguous, and the high-level mention of cost components doesn't clarify their roles. With 9 required parameters, this is a significant gap.

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 ('Estimate') and resource ('what a RAG pipeline costs'), enumerating three cost components (one-time corpus embedding, monthly vector storage, per-query LLM generation). This clearly distinguishes it from sibling calculators like llm_api_cost_calculator or fine_tuning_cost_calculator.

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 implies usage for RAG pipeline cost estimation but provides no explicit when-to-use guidance or alternatives. It doesn't mention when to use this over llm_api_cost_calculator, which might cover only LLM generation without embedding/storage costs.

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