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

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds value beyond that by disclosing the return payload's contents — including the non-obvious availability flag for semantic search, which an agent would otherwise have to discover empirically. It does not discuss cost, caching, or freshness, keeping it short of a 5.

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?

A single sentence, front-loaded with the verb and resource, followed by a compact enumeration of return fields and one clause on usefulness. No filler or repetition of the title.

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 no parameters, no output schema and a simple read-only surface, the description covers what an agent needs: what is counted and what filters can be discovered. Minor gap: no mention of response shape or whether counts are live/approximate, though this is low risk for a stats call.

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?

Zero parameters, so there is nothing to disambiguate; the baseline for a no-arg tool applies. Nothing in the description is needed or wasted on parameter semantics.

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?

States a specific verb (summarize) and resource (the vault), then enumerates exactly what the summary contains: concept/edge counts, products, tags, concept-type breakdown, and semantic-search availability. No sibling tool (list_concepts, search_concepts, get_context) produces aggregate statistics, so it is easy to distinguish from the rest of the toolset.

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?

"Useful for gauging how much knowledge is here and which product/tag filters exist" implies a discovery/orientation use case, which is real guidance. But it never says when not to use it or routes to alternatives (e.g., use list_concepts to enumerate contents vs. this for aggregates), so the agent must infer the boundary.

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