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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It discloses the authentication requirement ('Requires a Knox Bearer API key on the Authorization header — unauthenticated calls are rejected'), the exact return artifacts (Knox anchor record, C2PA-aligned envelope, FRE 902(13)/(14)-shape affidavit), and the tool's non-adjudicative, non-enforcement posture. This is substantial behavioral context beyond the schema.

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 front-loaded with the core purpose, then efficiently covers modes, return outputs, legal disclaimers, and authentication in roughly five sentences. Every sentence earns its place, and the structure moves from what the tool does to how to call it.

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 has no output schema and no annotations, the description is remarkably complete: it explains return values, mode-specific behavior, authentication prerequisites, and limitations. The input schema already covers parameter details, so nothing essential for correctly invoking the tool is missing.

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%, so the baseline is 3, but the description adds meaningful semantics beyond the schema by explaining the source modes with concrete examples: human_original (camera capture, original writing, recording), ai_generated (model + prompt + parameters anchored), and ai_assisted_hybrid (human-AI collaboration with edit chain). This enriches the source enum and the mode-specific nested objects beyond their raw names.

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 a specific verb and resource: 'Anchor any creation event to the Knox event chain and return a provenance bundle.' This clearly distinguishes it from the sibling verify_provenance by describing a creation/anchoring action rather than a verification action. It also enumerates the three supported creation modes, making the tool's scope immediately understandable.

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 gives clear context for when to use the tool: anchoring creation events, with explicit mode coverage for human_original, ai_generated, and ai_assisted_hybrid. It also states exclusions: it 'does not adjudicate authorship, does not grant copyright, and does not enforce any IP claim.' However, it does not explicitly point to verify_provenance as the alternative for verification, so it stops short of naming the sibling alternative.

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