Correlation Engine

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that the data is precomputed, that 'overview' is a rollup of all engines, and provides a drill-down usage pattern, which goes beyond the annotations without contradicting them.

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: the first states the core purpose, the second lists the engine options and provides a usage tip. Every word contributes; no redundancy or filler.

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 only one optional parameter and an output schema present, the description covers the essential workflow (overview first, then specific engine) and provides sufficient context for correct invocation. There are no obvious gaps.

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?

The schema covers the 'engine' parameter with an enum. The description enriches this by explaining that 'overview' is the default and a rollup of all engines, and advises using it first—semantic insight not present in the schema.

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 opens with a specific verb and resource: 'Read precomputed cross-dataset correlations.' It further distinguishes the tool by listing the available engines, which no sibling tool mentions, making the purpose unmistakable.

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?

Explicit workflow guidance is given: 'Use overview first to see what's available, then drill into a specific engine.' However, it does not contrast with sibling tools like get_correlation_path or explain_correlation, so it lacks explicit alternative selection.

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