spatial_sensitivity_analysis

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

No annotations exist, so the description carries the full disclosure burden. It states the output shape (cluster counts, noise counts, details per parameter set), which is useful, but says nothing about defaults when eps_values or min_samples_values are null, the computational cost of a grid sweep, or how coordinates (x/y/z) relate to system_name. For a 7-parameter analysis tool this is thin.

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, front-loaded with the action and followed by the return summary; no filler. It could be slightly tighter by merging the return clause, but nothing is wasted.

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?

An output schema exists, so the return-value sentence is optional but harmless. The critical gaps are input-selection semantics (coordinates vs system_name) and default behavior for the null-valued grid parameters, which matter a great deal for a 7-parameter tool with all parameters optional.

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 0% across 7 parameters, so the description must compensate and largely does not. 'Parameter combinations' hints that eps_values and min_samples_values form the sweep grid, but x, y, z, radius_ly, and system_name are entirely unexplained — an agent cannot tell whether to pass coordinates or a system name, or what radius_ly selects.

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 names a specific technique and resource: running DBSCAN across parameter combinations for sensitivity testing of spatial patterns. That is distinguishable from siblings like find_spatial_clusters (single clustering run) and graph_sensitivity_analysis (graph-domain counterpart), though neither alternative is named explicitly.

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?

'For sensitivity testing' implicitly frames the use case as robustness checking of clustering results, but there is no explicit when-to-use versus find_spatial_clusters or graph_sensitivity_analysis, and no stated prerequisites or exclusions. The agent must infer the routing decision.

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