Calculate agreement levels between multiple data sources to measure consensus in decision intelligence, using multi-source scoring for signal convergence analysis.
Multi-source agreement scoring. How much do different signals/sources agree?
| Name | Required | Description | Default |
|---|---|---|---|
| distributions | Yes | [{sourceId, values: number[]}] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, yet description omits output format, scoring range/interpretation, and computational 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?
Brief but redundant: second sentence questions what first already stated, wasting limited descriptive space.
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?
Lacks output schema and fails to describe return values, scoring methodology, or result interpretation.
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 with technical structure; description adds no parameter context but meets baseline.
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?
States it measures multi-source agreement but fails to clarify what 'convergence' specifically calculates (correlation, variance, consensus?) or distinguish from sibling analyze/detect tools.
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?
Provides no guidance on when to use versus analyze_risk, detect_anomaly, or other analytical tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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