AI Business System Advisor

by io.github.tenkai2018

Evaluate AI Opportunities

evaluate_ai_opportunities

Read-onlyIdempotent

Evaluates candidate AI workflow ideas for business value, implementation readiness, repeatability, trust/control risk, warnings, and missing information.

Input Schema

TableJSON Schema

Name	Required	Description
`aiIdea`	No	Specific AI or automation idea the user wants evaluated.
`businessType`	No	Type of business being reviewed.
`riskConcerns`	No	Concerns about customer trust, brand risk, money, privacy, compliance, or quality control.
`currentProblem`	No	Main problem the AI opportunities should help solve.
`businessContext`	No	Short description of the business, customers, offer, and current operating context.
`currentWorkflow`	No	Current workflow before AI or automation.
`candidateUseCases`	No	Candidate AI workflow ideas to evaluate.

Output Schema

TableJSON Schema

Name	Required	Description
`warnings`	Yes	Warnings about high-risk, premature, or unsafe automation patterns.
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`opportunities`	Yes	Evaluated AI opportunities with value, readiness, risk, and control guidance.
`missingInformation`	Yes	Information the user should provide to improve diagnostic confidence.
`recommendedFirstOpportunity`	No	Best first opportunity to consider based on value, readiness, and risk.

Schema Changelog

Changes observed during successful MCP inspections.

Changed21 schema fields changedJul 27, 2026

addedInput schema / properties / aiIdea / description

Added value: +"Specific AI or automation idea the user wants evaluated."

addedInput schema / properties / businessContext / description

Added value: +"Short description of the business, customers, offer, and current operating context."

addedInput schema / properties / businessType / description
```
Added value: +"Type of business being reviewed."
```
addedInput schema / properties / candidateUseCases / description
```
Added value: +"Candidate AI workflow ideas to evaluate."
```

changedInput schema / properties / candidateUseCases / items / anyOf

Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "properties": {
-      "availableData": {
-        "type": "string"
-      },
-      "currentControls": {
-        "type": "string"
-      },
-      "customerFacing": {
-        "type": "boolean"
-      },
-      "description": {
-        "type": "string"
-      },
-      "expectedImpact": {
-        "type": "string"
-      },
-      "name": {
-        "type": "string"
-      },
-      "riskIfWrong": {
-        "type": "string"
-      }
-    },
-    "required": [
-      "name",
-      "description"
-    ],
-    "type": "object"
-  }
-]New value: +[
+  {
+    "description": "Short free-text description of an AI use case the user is considering.",
+    "type": "string"
+  },
+  {
+    "properties": {
+      "availableData": {
+        "description": "Data, examples, documents, or systems available to support the use case.",
+        "type": "string"
+      },
+      "currentControls": {
+        "description": "Existing human review, approval, QA, or escalation controls.",
+        "type": "string"
+      },
+      "customerFacing": {
+        "description": "Whether the use case affects customers directly.",
+        "type": "boolean"
+      },
+      "description": {
+        "description": "What the candidate use case would do.",
+        "type": "string"
+      },
+      "expectedImpact": {
+        "description": "Expected business impact if the use case works well.",
+        "type": "string"
+      },
+      "name": {
+        "description": "Short name of the candidate AI use case.",
+        "type": "string"
+      },
+      "riskIfWrong": {
+        "description": "What could go wrong if the AI output or action is incorrect.",
+        "type": "string"
+      }
+    },
+    "required": [
+      "name",
+      "description"
+    ],
+    "type": "object"
+  }
+]

addedInput schema / properties / currentProblem / description

Added value: +"Main problem the AI opportunities should help solve."

addedInput schema / properties / currentWorkflow / description
```
Added value: +"Current workflow before AI or automation."
```

addedInput schema / properties / riskConcerns / description

Added value: +"Concerns about customer trust, brand risk, money, privacy, compliance, or quality control."

addedOutput schema / properties / confidence / description

Added value: +"Confidence level based on the clarity and completeness of the provided business context."

addedOutput schema / properties / missingInformation / description

Added value: +"Information the user should provide to improve diagnostic confidence."

addedOutput schema / properties / opportunities / description

Added value: +"Evaluated AI opportunities with value, readiness, risk, and control guidance."

addedOutput schema / properties / opportunities / items / properties / businessValueScore / description
```
Added value: +"Relative business value score from 1 to 5."
```
addedOutput schema / properties / opportunities / items / properties / implementationReadinessScore / description
```
Added value: +"Relative implementation readiness score from 1 to 5."
```
addedOutput schema / properties / opportunities / items / properties / name / description
```
Added value: +"Short name of the AI opportunity."
```
addedOutput schema / properties / opportunities / items / properties / priority / description
```
Added value: +"Recommended priority for this opportunity."
```
addedOutput schema / properties / opportunities / items / properties / recommendedFirstVersion / description
```
Added value: +"Safest narrow first version of the opportunity to pilot."
```
addedOutput schema / properties / opportunities / items / properties / requiredHumanControl / description
```
Added value: +"Human review, approval, or escalation rule required for this opportunity."
```
addedOutput schema / properties / opportunities / items / properties / summary / description
```
Added value: +"Public-safe explanation of the opportunity and why it matters."
```
addedOutput schema / properties / opportunities / items / properties / trustRiskLevel / description
```
Added value: +"Estimated risk level for the workflow or recommendation."
```

addedOutput schema / properties / recommendedFirstOpportunity / description

Added value: +"Best first opportunity to consider based on value, readiness, and risk."

addedOutput schema / properties / warnings / description

Added value: +"Warnings about high-risk, premature, or unsafe automation patterns."

First observedMay 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by listing evaluation criteria (business value, trust/control risk, etc.), but does not disclose additional behavioral traits beyond what annotations provide.

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, tight sentence that front-loads the verb and resource. Every phrase earns its place by specifying what the tool evaluates, with no wasted words.

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 tool has 7 parameters (all optional) and an output schema exists, the description is reasonably complete. It covers the core purpose without needing to explain return values. A slight deduction for not mentioning the output's role (e.g., that it returns an assessment report), but not required.

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 100% with all 7 parameters documented in the input schema. The description does not add parameter-specific details beyond the schema's own descriptions. Baseline 3 is appropriate as the schema carries the semantic 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 uses a specific verb ('Evaluates') and clearly identifies the resource ('candidate AI workflow ideas'). It enumerates the evaluation dimensions (business value, implementation readiness, etc.), which distinctively positions it from sibling tools that focus on narrower aspects like 'analyze_business_context' or 'assess_trust_control_risks'.

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

Usage Guidelines3/5

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

The description implies usage when candidate ideas need evaluation, but it does not explicitly state when to use this tool versus alternatives (e.g., when to use 'assess_trust_control_risks' instead). No 'when not' guidance or exclusion criteria are provided, leaving the agent to infer context.

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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