citation_provenance

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

With no annotations, the description fully bears the burden: it explains multiple engine queries, output structure per URL, consensus_urls as URLs cited by all engines, and how to interpret engine_count (high=strong, 1=engine-specific). No behavioral surprises.

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?

Two sentences, front-loaded with purpose, no filler. Every word contributes meaning, making it efficient and easy to parse.

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?

No output schema, but description fully explains return structure (list of URLs with engines, consensus_urls) and interpretation. Covers all necessary context for an agent to invoke and understand results.

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%, baseline 3. The description adds value: explains default engines (all LLM with keys) and when to include bing/brave serp, plus default and role of max_results. This goes beyond schema definitions.

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 fans out a query across multiple AI engines and reports per-URL cross-engine consensus, directly distinguishing it from sibling tools like check_citations (single citation check) or citation_evidence (evidence gathering).

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 when to use (need cross-engine consensus) and explains output interpretation. However, it doesn't explicitly list alternatives or when not to use, though sibling context and description hint at differentiation.

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