AI Business System Advisor

by io.github.tenkai2018

Identify Bottlenecks

identify_bottlenecks

Read-onlyIdempotent

Identifies likely revenue, operations, customer experience, and trust/control bottlenecks from the supplied business context and returns a public-safe summary.

Input Schema

TableJSON Schema

Name	Required	Description
`aiIdea`	No	Optional AI or automation idea the user is considering.
`metrics`	No	Known metrics such as response time, conversion, close rate, cycle time, churn, cost, or error rate.
`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 or bottleneck the user suspects.
`teamPainPoints`	No	Internal pain points such as repetitive work, slow handoffs, rework, or unclear ownership.
`businessContext`	No	Short description of the business model, customers, offer, team, and operating context.
`currentWorkflow`	No	Current workflow steps, handoffs, tools, and review points.
`customerComplaints`	No	Known customer complaints, friction points, refunds, or support issues.

Output Schema

TableJSON Schema

Name	Required	Description
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`bottleneckSummary`	Yes	Concise public-safe summary of the most important bottlenecks.
`missingInformation`	Yes	Information the user should provide to improve diagnostic confidence.
`revenueBottlenecks`	Yes	Likely issues that reduce sales, conversion, qualified calls, deal speed, or revenue growth.
`mostLikelyRootCause`	Yes	Most likely underlying cause connecting the visible bottlenecks.
`operationalBottlenecks`	Yes	Likely issues that slow delivery, increase manual work, create rework, or reduce efficiency.
`trustControlBottlenecks`	Yes	Likely gaps in review rules, approvals, escalation triggers, data boundaries, or quality control.
`customerExperienceBottlenecks`	Yes	Likely issues that create customer friction, unclear expectations, slow responses, or inconsistent service.

Schema Changelog

Changes observed during successful MCP inspections.

Changed17 schema fields changedJul 27, 2026

addedInput schema / properties / aiIdea / description

Added value: +"Optional AI or automation idea the user is considering."

addedInput schema / properties / businessContext / description

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

addedInput schema / properties / businessType / description
```
Added value: +"Type of business being reviewed."
```

addedInput schema / properties / currentProblem / description

Added value: +"Main problem or bottleneck the user suspects."

addedInput schema / properties / currentWorkflow / description

Added value: +"Current workflow steps, handoffs, tools, and review points."

addedInput schema / properties / customerComplaints / description

Added value: +"Known customer complaints, friction points, refunds, or support issues."

addedInput schema / properties / metrics / description

Added value: +"Known metrics such as response time, conversion, close rate, cycle time, churn, cost, or error rate."

addedInput schema / properties / riskConcerns / description

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

addedInput schema / properties / teamPainPoints / description

Added value: +"Internal pain points such as repetitive work, slow handoffs, rework, or unclear ownership."

addedOutput schema / properties / bottleneckSummary / description

Added value: +"Concise public-safe summary of the most important bottlenecks."

addedOutput schema / properties / confidence / description

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

addedOutput schema / properties / customerExperienceBottlenecks / description

Added value: +"Likely issues that create customer friction, unclear expectations, slow responses, or inconsistent service."

addedOutput schema / properties / missingInformation / description

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

addedOutput schema / properties / mostLikelyRootCause / description

Added value: +"Most likely underlying cause connecting the visible bottlenecks."

addedOutput schema / properties / operationalBottlenecks / description

Added value: +"Likely issues that slow delivery, increase manual work, create rework, or reduce efficiency."

addedOutput schema / properties / revenueBottlenecks / description

Added value: +"Likely issues that reduce sales, conversion, qualified calls, deal speed, or revenue growth."

addedOutput schema / properties / trustControlBottlenecks / description

Added value: +"Likely gaps in review rules, approvals, escalation triggers, data boundaries, or quality control."

First observedMay 30, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds the detail that it returns a 'public-safe summary', which is behavioral context not present in 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?

Single sentence, front-loaded with action and resource. No wasted words and clearly structured.

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 number of optional parameters and that an output schema exists, the description covers the core purpose adequately. It does not explain parameter interactions, but the annotations and schema provide sufficient context.

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%, so the schema fully documents each parameter. The description does not add additional meaning beyond the schema. Baseline 3 applies.

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 ('identifies'), clearly states the resource ('bottlenecks'), and lists the categories (revenue, operations, etc.), distinguishing it from siblings like 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?

No explicit when-to-use or when-not-to-use guidance. There are no exclusions or alternatives mentioned. The description implies it should be used when business context is supplied, but lacks contextual boundaries.

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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