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

Annotations declare readOnly/openWorld/idempotent hints, and the description adds substantial behavioral context beyond them: the refusal protocol with enumerated refusal_reason values (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), the verbatim-evidence constraint, and the extra-LLM-call cost. This is rich, non-redundant behavioral disclosure.

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?

Dense but каждое sentence earns its place: purpose, mechanism, success return shape, refusal return shape, when-to-use with examples, and cost tradeoff. Front-loaded with the core purpose, and structured as needed for a high-complexity tool with no output schema.

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?

High-complexity tool with no output schema, and the description fully compensates: it documents both the success return shape (answer, evidence, confidence, source, fetched_at) and the refusal shape with all five refusal_reason values. The agent has everything needed to call it and interpret its result correctly.

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% — all 6 parameters (question plus 5 aliases) are fully documented in the schema. The description adds only context about the query's breadth (routing across 5,798 tools) but no parameter-level syntax or format details, so the baseline of 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 opens with 'Hallucination-resistant answer mode for high-stakes reads,' a specific verb-resource pair that immediately distinguishes it from sibling ask_pipeworx. It further clarifies the mechanism (same routing, extracts answer using ONLY the tool result), making the tool's identity unmistakable even without seeing sibling names.

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 states when to use ('whenever an answer will be quoted, cited, or acted on... must not invent facts') with concrete examples (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative (ask_pipeworx) and the condition for preferring it ('casual lookups'), leaving zero ambiguity.

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