madeonsol_signal_performance

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by specifying the exact metrics returned (hit rate, sample size, median outcome, confidence window) and the context of evaluation, which goes beyond the annotations.

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 with no wasted words: first sentence defines output, second sentence provides usage guidance. Information is front-loaded and every sentence earns its place.

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?

Given the simple one-parameter input, no output schema, and comprehensive annotations, the description fully covers what the tool returns and when to use it. No additional details are needed for an AI agent to select and invoke it correctly.

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 has 100% coverage for the single 'name' parameter with a brief description. The tool description adds examples and context ('evaluate how well a signal...'), reinforcing the parameter's meaning and usage beyond 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?

Description clearly states the tool returns signal performance stats including hit rate, sample size, median outcome, and confidence window. It gives specific signal name examples and distinguishes this from sibling tools like madeonsol_kol_coordination, which focus on coordination rather than performance.

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?

Description explicitly says 'Use this to evaluate how well a signal...has been predicting token moves before acting on it,' providing clear context for when to apply the tool. It does not explicitly exclude alternatives, but the purpose is sufficiently scoped.

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