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

Annotations declare readOnlyHint and openWorldHint. The description complements this by detailing the pipeline steps (builds context, generates SQL, validates, executes read-only, interprets), and specifies the return format (Markdown report with components). No contradictions.

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 a single, well-structured paragraph that front-loads the purpose. Every sentence adds value, though it could be slightly more concise without losing key details.

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 (end-to-end NL to answer), the description covers the full pipeline, return format, prerequisites (LLM provider), and alternatives. With 100% schema coverage, useful annotations, and an output schema, the description is fully adequate.

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 both parameters are well-described in the schema. The description adds no new parameter-level semantics but contextualizes their use. 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's purpose as 'Answer a natural-language question end-to-end', specifying the verb and resource. It distinguishes from siblings by naming alternatives like get_semantic_context+run_sql and generate_sql.

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 gives usage guidance: 'Use it when you want a finished answer rather than raw rows' and provides alternative paths for manual control or SQL-only generation. It also notes the requirement for an LLM provider.

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