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tenkai2018

ai-business-system-advisor-mcp

by tenkai2018

Assess Trust And Control Risks

assess_trust_control_risks
Read-onlyIdempotent

Reviews proposed AI workflows for trust and control risks by analyzing human oversight, data boundaries, quality controls, escalation triggers, and automation safety.

Instructions

Reviews a proposed AI workflow for human review needs, data boundaries, quality controls, escalation triggers, unsafe automation risks, and confidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aiIdeaNoSpecific AI or automation idea the user is considering.
businessTypeNoType of business being reviewed.
riskConcernsNoKnown concerns about customer trust, brand risk, compliance, money, privacy, or quality control.
workflowIdeaNoAI workflow or agent idea to assess for trust and control risk.
canAffectMoneyNoWhether the workflow can influence pricing, refunds, payments, scope, or financial decisions.
currentProblemNoMain business or workflow problem the user wants to solve.
customerFacingNoWhether the workflow directly affects customers or customer-facing messages.
currentControlsNoExisting review, approval, QA, escalation, or monitoring controls.
currentWorkflowNoCurrent workflow before AI or automation.
proposedWorkflowNoProposed workflow design, including what AI would do and what humans would review.
usesSensitiveDataNoWhether the workflow may use private, regulated, or sensitive business/customer data.
canAffectBrandTrustNoWhether the workflow can affect brand reputation, customer expectations, or public trust.
requiresExpertJudgmentNoWhether the workflow needs professional, strategic, legal, financial, HR, or domain judgment.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
riskLevelYesEstimated risk level for the workflow or recommendation.
confidenceYesConfidence level based on the clarity and completeness of the provided business context.
riskSummaryYesConcise public-safe summary of the main trust and control risks.
humanReviewRulesYesRules for when a human must review, approve, or override AI output.
requiredControlsYesControls needed before the workflow should be piloted or expanded.
escalationTriggersYesConditions that should escalate to a human owner or expert reviewer.
missingInformationYesInformation the user should provide to improve risk assessment confidence.
dataBoundaryWarningsYesWarnings about sensitive data, privacy, access boundaries, or inappropriate inputs.
notRecommendedActionsYesActions that should not be automated in the current version.
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, indicating no side effects. The description adds value by specifying the topics reviewed (e.g., human review needs, escalation triggers), which helps the agent understand the tool's scope 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.

Conciseness5/5

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

The description is a single, front-loaded sentence of 15 words that efficiently communicates the tool's purpose and scope, with no wasted words or redundant information.

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?

Given the high parameter count (13) and full schema coverage, plus an output schema (implied), the description adequately summarizes the tool's input scope. However, it could briefly mention the output type or purpose (e.g., 'returns a risk assessment') to improve completeness, but the absence is not critical due to the output 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 coverage is 100%, so each parameter already has a description. The tool description only summarizes the parameters (e.g., 'data boundaries', 'quality controls') without adding new meaning or clarifying relationships, so it meets the baseline but does not exceed it.

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 clearly states the verb 'reviews' and the resource 'proposed AI workflow', and enumerates specific aspects (human review needs, data boundaries, etc.) that the tool covers, distinguishing it from sibling tools that focus on other aspects like business context or touchpoints.

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?

There is no explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or compare with sibling tools, leaving the agent to infer usage from the tool's name and description alone.

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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