daf-jev

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jev_composite_score

Calculates the expected value of a score distribution across level indices, optionally applying positional weights to produce a re-weighted composite score from input probabilities.

Instructions

Expected value of a score distribution over its level indices.

probabilities maps level index (as a string) to probability. With weights (one per level, positional order matching the sorted level indices) the distribution is re-weighted as in :func:daf_jev.compose.composite_score. Requires at least 2 levels; probabilities must be finite and non-negative and keys integer level indices (ValueError otherwise). provider is validated against the registry; local math is provider-agnostic.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "probabilities": "<probabilities>"
    },
    "name": "jev_composite_score"
  }
}

Input Schema

Name	Required	Default
`weights`	No
`provider`	No	jev
`probabilities`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.2Nov 9, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations, the description carries the full behavioral burden and discloses important validation rules: at least 2 levels are required, probabilities must be finite and non-negative with integer level-index keys, and invalid input raises ValueError. It also notes that provider is validated against the registry while local math is provider-agnostic, adding useful context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core operation and then details input constraints efficiently. It is dense but not wasteful, though the RST function reference may be opaque to some agents.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a pure mathematical computation with an output schema, the description covers purpose, parameter semantics, and validation behavior adequately. It lacks usage guidance relative to alternatives, but the core information needed to invoke the tool correctly is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: probabilities maps level index strings to probabilities, weights are positional per sorted level index, and provider is validated against the registry. It does not explain the default provider value or registry details, but it adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool computes the expected value of a score distribution over its level indices. It is specific about the operation and inputs, but it does not differentiate this tool from sibling tools or name alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides input constraints but does not explain when to use this tool versus alternatives. There is no explicit when-to-use or when-not-to-use guidance, leaving the agent to infer usage from the mathematical purpose.

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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