Correlation Engine

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

Annotations already cover read-only, idempotent, and non-destructive behavior, so the safety profile is clear. The description adds that the data is 'precomputed' and that 'overview' is a rollup across engines, which is useful behavioral context beyond annotations. It doesn't describe the response envelope, but an output schema exists and the parameter schema covers summary/paging behavior.

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 core purpose ('Read precomputed cross-dataset correlations') is front-loaded, followed by the engine list and the drill-down workflow. No filler or repetition of structural data.

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?

For a three-parameter read tool with full schema coverage and an output schema, the description supplies the missing conceptual layer: what the engines are and the recommended access pattern. It feels complete enough for correct invocation, only lacking any comparative positioning against sibling correlation tools.

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 100%: all three parameters (limit, engine, offset) have detailed descriptions including enums and paging notes. The description adds strategy ('use overview first') but not parameter meaning beyond the schema, so baseline 3 is appropriate.

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 clear verb ('Read') and a clear resource ('precomputed cross-dataset correlations'), and enumerates all engine choices. It does not explicitly contrast itself with the many correlation-related siblings like get_correlation_path or explain_correlation, so it stops short of a full sibling-differentiation 5.

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 description gives concrete guidance: use 'overview' first, then drill into a specific engine. This is useful, explicit, intra-tool routing. However, it does not tell an agent when to choose this tool over the many sibling correlation tools, so it misses exclusion/alternative guidance.

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