dante.remediate_hallucination

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

With no annotations, the description carries the full burden. It says what artifacts are generated but not whether anything is published or written externally, whether the output is returned inline, what permissions or rate limits apply, or whether generation is deterministic. For a content-generation tool with zero annotation coverage this leaves meaningful behavioral gaps.

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?

A single dense sentence that front-loads the verb and enumerates the deliverables without filler. It is efficient, though the parenthetical artifact list slightly crowds out more useful routing information.

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?

Seven parameters (five required), no output schema, and no annotations make this a complex tool, yet the description says nothing about how inputs shape the output or what the caller receives. It names the artifacts but not the return structure or the role of the five required inputs, leaving significant gaps for a tool of this complexity.

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 only 14% — just target_oracle is documented — and the description mentions no parameters at all. The phrase 'inaccurate AI claims' loosely gestures at hallucination_claim and verified_fact, but brand_name, domain_url, lang, and source_url_to_counter get no explanation in either place, so the description fails to compensate for the coverage 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 uses a specific verb ('Generate') and names the concrete artifacts produced (Markdown + HTML ClaimReview blocks, FAQPage JSON-LD, verification prompts) for a specific goal (remediating inaccurate AI claims). This is distinguishable from siblings like run_audit or forge_geo_shield, though it never explicitly contrasts itself with them.

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?

There is no statement of when to use this tool versus the sibling tools, no prerequisites, and no exclusions. The use case ('remediate inaccurate AI claims') is implied by the goal clause but the agent must infer the trigger conditions on its own.

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