inkog_generate_mlbom

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

No annotations are provided, so description carries full burden. It implies analysis of a codebase (path required) but does not disclose potential side effects like file system access, scan duration, or that it is a read-only operation. The description is not misleading but lacks depth.

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?

Three concise sentences that front-load the action and purpose. No wasted words; every sentence adds value.

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 3 parameters, no output schema, and no annotations, the description covers the core purpose, supported formats, and usage context. It could be improved by describing the output format or behavior for invalid paths, but it is largely complete.

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?

Input schema has 100% description coverage, so baseline is 3. The description adds minimal parameter nuance beyond what's in the schema, e.g., mentioning 'CycloneDX and SPDX formats' but those are already enumerated in the format parameter.

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?

Description clearly states the tool generates a Machine Learning Bill of Materials (MLBOM) for AI agents, listing models, tools, data sources, frameworks, and dependencies. It also mentions supported formats (CycloneDX, SPDX), making the purpose specific and distinct from sibling audit/scan tools.

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?

Explicitly says 'Use this when documenting AI agent dependencies for supply chain compliance,' providing clear context. However, it does not mention when not to use or suggest alternatives.

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