eDiscovery Decoder News/Calc

Calculate Control Set Recall

calculate_control_set_recall

Read-onlyIdempotent

Calculate recall against a known control set: the share of documents already confirmed relevant that the workflow found, with a Wilson confidence interval. Use when you have relevant-found and relevant-missed counts from a fixed reference set. For recall from a confusion matrix use calculate_review_metrics; from a discard-set sample use calculate_tar_recall_estimate. Aggregate counts only; not legal advice.

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": {
      "relevant_found": "<relevant_found>",
      "relevant_missed": "<relevant_missed>"
    },
    "name": "calculate_control_set_recall"
  }
}

Input Schema

Name	Required	Description	Default
`relevant_found`	Yes
`relevant_missed`	Yes
`confidence_level`	No

Output Schema

Name	Required	Description	Default
`recall`	Yes
`formula`	Yes
`relevantFound`	Yes
`totalRelevant`	Yes
`relevantMissed`	Yes
`confidenceLevel`	Yes
`confidenceInterval`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedNov 7, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```
- addedInput schema / properties / relevant_found / maximum
```
Added value: +9007199254740991
```
- addedInput schema / properties / relevant_missed / maximum
```
Added value: +9007199254740991
```
- addedOutput schema / properties / relevantFound / maximum
```
Added value: +9007199254740991
```
- addedOutput schema / properties / relevantMissed / maximum
```
Added value: +9007199254740991
```
- addedOutput schema / properties / totalRelevant / maximum
```
Added value: +9007199254740991
```
First observedJun 23, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuine context beyond that: the statistical method (Wilson confidence interval), the input constraint (aggregate counts only), and a scope disclaimer. It does not describe output shape or edge cases like zero denominators, but an output schema exists so that burden is lower.

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

Conciseness5/5

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

Three sentences total, with the definition front-loaded, followed by usage conditions, then sibling routing and scope caveats. No sentence restates the name or wastes space.

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?

With rich annotations, an output schema, and explicit sibling routing, an agent has nearly everything needed to select and call this tool. The one residual gap is that confidence_level's presence and default are not surfaced in the description and are undocumented in the schema.

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

Parameters3/5

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

Schema description coverage is 0%, so the description has to carry meaning, and it does partially: it names the two required count inputs and implies a confidence-interval computation. However, the third parameter (confidence_level, default 0.95) is never explicitly mentioned or explained, and nothing clarifies that counts must be non-negative integers.

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

Purpose5/5

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

States a specific verb and resource ('Calculate recall against a known control set') and defines the metric precisely as the share of confirmed-relevant documents the workflow found, including the Wilson interval. It explicitly names the two sibling alternatives (calculate_review_metrics, calculate_tar_recall_estimate) so the agent can distinguish it without opening schemas.

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

Usage Guidelines5/5

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

Gives an explicit when-to-use condition ('when you have relevant-found and relevant-missed counts from a fixed reference set') and routes to two named alternatives for the adjacent cases (confusion matrix → calculate_review_metrics, discard-set sample → calculate_tar_recall_estimate). Both inclusion and exclusion conditions are stated.

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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