hyperspace_analyze_geometry

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

No annotations exist, so the description carries the full burden. It usefully discloses the return shape ({delta, recommendation} with its enumerated values) and that one mode runs server-side, but it never mentions that a `samples` default of 200 implies an approximate/sampled estimate, nor cost, latency, or read-only safety of analyzing a whole collection.

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 tight sentences: the computation and its two input modes first, then the return contract. Nothing redundant, nothing buried.

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?

It covers the return shape well despite having no output schema, but for a 3-parameter tool with 33% schema coverage it leaves real gaps: `samples` is unexplained, the precedence between `collection` and `vectors` is undefined, and approximation behavior is undisclosed.

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 only 33%, with just `collection` documented there, so the description must compensate. It does add value by describing `collection` as server-side and clarifying `vectors` as an array of numeric arrays, but the `samples` parameter — which governs accuracy and runtime — is never mentioned at all.

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?

Names a specific computation (Gromov Delta-hyperbolicity) and the two input modes (server-side `collection` or explicit `vectors`), which is far more precise than any sibling name. It does not explicitly contrast itself with neighbors like hyperspace_analyze_thought_stability or hyperspace_find_clusters, but the resource is distinctive enough that an agent can tell them apart.

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 'either for a named collection ... or over supplied vectors' phrasing does route parameter selection, which is useful. However, it never says when an agent should reach for this tool versus the other analysis siblings, nor whether `collection` and `vectors` are mutually exclusive or which wins if both are given.

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