Santismm Knowledge — Harness Engineering, Agentic AI & Governance

Calculate human supervision capacity for an AI agent

calculate_human_supervision_capacity

Read-onlyIdempotent

Calculate review and escalation workload, required FTE, available headroom or backlog, monthly labour cost and sustainable case volume. Use this before production rollout to test whether the stated human-oversight model is operationally credible; the result uses averages and is not a queueing simulation.

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": {
      "volume": "<volume>",
      "sample": "<sample>",
      "reviewMinutes": "<reviewMinutes>",
      "escalationRate": "<escalationRate>",
      "escalationMinutes": "<escalationMinutes>",
      "workdays": "<workdays>",
      "hoursDay": "<hoursDay>",
      "utilization": "<utilization>",
      "reviewers": "<reviewers>",
      "hourlyCost": "<hourlyCost>"
    },
    "name": "calculate_human_supervision_capacity"
  }
}

Input Schema

Name	Required	Description
`locale`	No	Language for interpretations, assumptions, formulas and warnings. The Labs execution service currently resolves fr/de/ja/zh to English and reports that fallback.
`sample`	Yes	Share of all cases selected for routine review, in percent.
`volume`	Yes	Agent cases per month.
`hoursDay`	Yes	Paid hours per working day.
`workdays`	Yes	Working days per month.
`reviewers`	Yes	Available reviewer FTE.
`hourlyCost`	Yes	Fully loaded reviewer hourly cost, in the chosen currency.
`utilization`	Yes	Share of paid time available for review and escalation, in percent.
`reviewMinutes`	Yes	Minutes per routine review.
`escalationRate`	Yes	Share of cases escalated, in percent.
`escalationMinutes`	Yes	Minutes per escalation.

Output Schema

Name	Required	Description
`slug`	Yes
`units`	Yes
`inputs`	Yes
`source`	Yes
`api_url`	Yes
`license`	Yes
`results`	Yes
`updated`	Yes
`version`	Yes
`fallback`	Yes
`formulas`	Yes
`language`	Yes
`warnings`	Yes
`assumptions`	Yes
`canonical_url`	Yes	Cite this URL.
`interpretation`	Yes
`schema_version`	Yes
`methodology_url`	Yes
`resolved_locale`	Yes
`requested_locale`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 6, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the bar for additional behavioral disclosure is lower. The description adds a valuable caveat: 'the result uses averages and is not a queueing simulation.' This prevents the agent from over-trusting the output as a real-time simulation. No contradiction with annotations exists.

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 with no filler. The first sentence front-loads the core outputs, the second gives the operational context, and the third states an essential limitation. Every sentence earns its place.

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

Completeness5/5

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

For a moderately complex 11-parameter calculation tool, the description provides purpose, use-case timing, and a critical methodological limitation. The output schema and fully described input schema cover the remaining details. An agent has enough context to select and invoke this tool correctly.

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 coverage is 100%, so every parameter is already documented with type, range, and meaning. The description adds no per-parameter detail, but it does summarize the output dimensions (FTE, cost, headroom/backlog) that tie the parameters together. This matches the baseline expected when the schema carries the descriptive load.

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?

The description names a specific verb ('Calculate') and resource ('human supervision capacity'), then enumerates concrete outputs: review and escalation workload, required FTE, headroom/backlog, monthly labour cost, and sustainable case volume. This clearly differentiates it from sibling calculation tools like calculate_agent_economics or calculate_evaluation_sample_size. The purpose is unambiguous and immediately actionable.

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

Usage Guidelines4/5

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

The description explicitly states when to use the tool: 'before production rollout to test whether the stated human-oversight model is operationally credible.' This gives clear contextual guidance. It does not explicitly name alternatives or state when not to use it, so it stops short of a full 5.

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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