lbb_models

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

Annotations already mark readOnlyHint=true, so no contradiction. Description adds context about operating 'over one pinned published snapshot' and explains action-specific behavior (shadow_eval body, dataset returns bounded examples). This adds value beyond annotations.

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 with no wasted words, but the information could be better structured (e.g., separating action types into a list). Still efficient.

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 exists, so description should clarify return values, but it doesn't. It also omits explanation of the 'limit', 'graph', 'branch', and 'detail' parameters. While it covers the two action groups, it is incomplete for a 7-parameter tool with nested objects.

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 43%, so the schema leaves gaps. The description partially compensates by explaining the 'shadow_eval' action requires the body parameter and that dataset actions use an optional signal split, but does not cover graph, branch, limit, or detail parameters.

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 reads model-training inputs or compares retrieval configurations, using verbs like 'Read' and 'compare'. It distinguishes from sibling tools by focusing on model-training and snapshots, but could be more specific about the scope.

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?

No guidance on when to use this tool versus siblings like lbb_query or lbb_inspect. The description lacks explicit context for selection criteria.

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