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

Annotations already cover readOnly/idempotent/destructive/openWorld, so the bar is lower; the description still adds required scopes (analytics:read, luca:read), data sensitivity redacted, the underlying endpoint, and the honest-empty-state behavior. It adds real operational context beyond the 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?

Purpose and payload are front-loaded, then operational facts (endpoint, scopes, idempotency, workspace override) follow in compact lines. It is a bit list-heavy but every line carries information an agent can use.

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?

With an output schema present, return-value documentation is not strictly required, yet the description still summarizes the shape and the empty-state semantics. Auth, workspace targeting, and scope requirements are covered, making it complete enough to invoke correctly.

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 67%: workspaceId/workspaceSlug are documented in the schema, and the description mirrors that they override the environment default workspace. The start/end period parameters are only obliquely referenced via 'for a period' and left entirely to the schema, so the description adds little beyond structured fields.

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 states a specific verb+resource ('read-only rollup of agent-improvement signals mined from post-call feedback') and enumerates the payload (counts, per-type breakdown, example notes). It is clear about scope, but it never distinguishes itself from near-neighbors like luca_learning_panel, luca_learning_framework_performance, or luca_analytics_insights.

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?

It provides useful context that an empty rollup is the expected state until feedback is submitted, and notes workspace override, but it gives no explicit when-to-use or when-not-to-use guidance relative to the many sibling analytics/learning tools. Usage is implied rather than directed.

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