How do I use ai-business-system-advisor-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ai-business-system-advisor-mcp Review my business and recommend a safe first AI workflow." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

ai-business-system-advisor-mcp

by tenkai2018

Overview Schema Source Related Servers Score Discussions Admin

Hybrid

AI Business System Advisor MCP

Find the safest first AI workflow for your business.

AI Business System Advisor MCP helps founders, operators, consultants, and small teams review business workflows, AI opportunities, trust/control risks, and implementation readiness before building AI agents or automation.

It produces a public-safe mini review, recommends a practical first AI-human workflow, and exports a structured intake packet for deeper review or implementation planning.

This MCP is designed for business-system thinking, not generic chatbot recommendations or tool-first automation.

What It Does

Reviews business context, target customers, offers, workflows, and goals.
Identifies operational bottlenecks and customer journey friction.
Evaluates AI workflow ideas for value, readiness, and trust/control risk.
Recommends a narrow first AI-human workflow with review and escalation rules.
Generates mini reports and structured intake packets.

Related MCP server: Cordum_io

Who It Is For

Solo founders
Service businesses
B2B consultants
Agency owners
Operators
Small teams exploring AI agents or automation

Problems It Helps Solve

Which workflow should I automate first?
Is this AI agent idea safe for my customer experience?
Where should humans stay in control?
What trust/control risks exist in this workflow?
What information is missing before implementation?
How should I prepare this business for AI automation?

Use With MCP Clients

For Claude Desktop, Cursor, Windsurf, Codex, or another stdio MCP client:

{
  "mcpServers": {
    "ai-business-system-advisor": {
      "command": "npx",
      "args": ["-y", "ai-business-system-advisor-mcp"]
    }
  }
}

You can also run the package directly:

npx -y ai-business-system-advisor-mcp

For remote MCP clients, use:

https://mcp.prodxsolution.com/ai-business-system-advisor/mcp

Smithery listing:

https://smithery.ai/servers/prodxsolution/ai-business-system-advisor-mcp

Available Tools

analyze_business_context
map_customer_touchpoints
identify_bottlenecks
evaluate_ai_opportunities
assess_trust_control_risks
recommend_first_workflow
generate_mini_report
recommend_next_step
export_intake_packet

Available Prompts

run_mini_business_system_review
evaluate_ai_workflow_idea
prepare_diagnostic_intake
governance_gap_snapshot

Available Resources

Overview
How it works
Readiness guide
Sample mini report
Sample intake packet
Privacy note

Example Questions

Ask your AI assistant:

Review my business and identify the safest first AI workflow to improve.
Is this AI agent idea safe for customer support, refunds, and complaints?
Where should humans stay in control in this workflow?
What trust/control risks should I consider before automating this process?
Generate a mini business system review and tell me what information is missing.
Export an intake packet for deeper workflow review.

Example Output

Mini Business System Review

Business Snapshot:
A service agency sells conversion and landing page work to ecommerce brands. Sales capacity is constrained by manual lead research, inconsistent qualification, and proposal drafting.

Likely Bottlenecks:
- Lead research takes too long before a sales call.
- Proposal quality depends on expert availability.
- Customer-facing claims need clearer approval rules.

Recommended First Workflow:
AI prepares a lead research and qualification brief, then drafts proposal sections from approved language. A human reviews lead fit, scope, pricing, claims, and final customer-facing messages.

Trust And Control Risk:
Medium. The workflow can affect revenue, brand trust, and customer expectations, so AI should prepare work but not send promises or pricing without approval.

Success Metrics:
- qualified calls booked
- proposal cycle time
- human edit rate
- lead-to-call conversion

Missing Information:
ideal customer criteria, approved claims, disallowed claims, proposal rules, escalation triggers, and success metrics

Privacy And Security

The server is read-mostly and does not intentionally persist submitted business context. Do not send passwords, API keys, raw private customer records, regulated data, or confidential contracts.

Outputs are first-pass reviews based on the information provided. They are not legal, financial, medical, HR, or compliance advice.

Public Safety Boundary

The server uses business-facing language and refuses requests for private implementation details. Public outputs focus on business context, workflow fit, risk controls, recommended next steps, and missing information.

It does not expose protected implementation details, exact formulas, confidential designs, or client-specific confidential data.

Roadmap

More fictional sample reviews for common business types.
Guided public workflow for mini reviews and intake packets.
Additional public resources for AI workflow readiness and human review design.

Available Tools

9 tools

analyze_business_contextAnalyze Business ContextB

Read-onlyIdempotent

Inspect

Summarizes the supplied business context, including customer, offer, workflow, goals, constraints, readiness signals, missing information, and confidence.

ParametersJSON Schema

Name	Required	Description
`notes`	No	Any additional context the user wants considered in the review.
`offer`	No	Core product, service, package, or outcome the business sells.
`aiIdea`	No	AI or automation idea the user is considering.
`teamSize`	No	Approximate team size and key roles involved in the workflow.
`goal90Days`	No	Desired business or workflow outcome over the next 90 days.
`constraints`	No	Known constraints such as budget, team capacity, tools, timeline, compliance needs, or data access.
`currentGoal`	No	Current business goal the user wants the review to support.
`businessType`	No	Type of business, such as ecommerce, SaaS, consulting, agency, local service, or solo business.
`revenueModel`	No	How the business earns revenue, such as projects, retainers, subscriptions, services, or products.
`riskConcerns`	No	Concerns related to customer trust, brand reputation, legal exposure, sensitive data, or quality control.
`currentProblem`	No	Main business or workflow problem the user wants to solve.
`targetCustomer`	No	Primary customer segment or buyer the business serves.
`currentWorkflow`	No	How the relevant workflow currently works, including manual steps, tools, handoffs, and review points.

Output Schema

ParametersJSON Schema

Name	Required	Description
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`businessSnapshot`	Yes	Concise public-safe summary of the business and operating context.
`readinessSignals`	Yes	Signals that indicate whether the business is ready for AI-assisted workflow design.
`missingInformation`	Yes	Information the user should provide to improve diagnostic confidence.
`likelyBusinessModel`	Yes	Likely business model inferred from the supplied context.
`targetCustomerSummary`	Yes	Short summary of the customer segment or buyer context.
`valuePromiseHypothesis`	Yes	Hypothesis about the core value promise or outcome the business sells.
`primaryConstraintHypothesis`	Yes	Likely main constraint limiting progress or readiness.

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds no behavioral context beyond listing output elements like confidence and missing information.

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

Conciseness4/5

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

Single sentence, no fluff. However, the list of included items is somewhat generic and could be more structured.

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

Completeness3/5

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

With many optional parameters and an output schema, the description is adequate but vague. It does not explain how missing information or confidence is handled.

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?

All 13 parameters have schema descriptions (100% coverage), so the tool description adds no additional meaning. Baseline 3 is appropriate.

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 tool summarizes business context and lists specific elements (customer, offer, workflow, etc.), distinguishing it from sibling tools that focus on specific aspects like bottlenecks 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?

No guidance on when to use this tool versus alternatives like map_customer_touchpoints or evaluate_ai_opportunities. The description does not specify prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

assess_trust_control_risksAssess Trust And Control RisksA

Read-onlyIdempotent

Inspect

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

ParametersJSON Schema

Name	Required	Description
`aiIdea`	No	Specific AI or automation idea the user is considering.
`businessType`	No	Type of business being reviewed.
`riskConcerns`	No	Known concerns about customer trust, brand risk, compliance, money, privacy, or quality control.
`workflowIdea`	No	AI workflow or agent idea to assess for trust and control risk.
`canAffectMoney`	No	Whether the workflow can influence pricing, refunds, payments, scope, or financial decisions.
`currentProblem`	No	Main business or workflow problem the user wants to solve.
`customerFacing`	No	Whether the workflow directly affects customers or customer-facing messages.
`currentControls`	No	Existing review, approval, QA, escalation, or monitoring controls.
`currentWorkflow`	No	Current workflow before AI or automation.
`proposedWorkflow`	No	Proposed workflow design, including what AI would do and what humans would review.
`usesSensitiveData`	No	Whether the workflow may use private, regulated, or sensitive business/customer data.
`canAffectBrandTrust`	No	Whether the workflow can affect brand reputation, customer expectations, or public trust.
`requiresExpertJudgment`	No	Whether the workflow needs professional, strategic, legal, financial, HR, or domain judgment.

Output Schema

ParametersJSON Schema

Name	Required	Description
`riskLevel`	Yes	Estimated risk level for the workflow or recommendation.
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`riskSummary`	Yes	Concise public-safe summary of the main trust and control risks.
`humanReviewRules`	Yes	Rules for when a human must review, approve, or override AI output.
`requiredControls`	Yes	Controls needed before the workflow should be piloted or expanded.
`escalationTriggers`	Yes	Conditions that should escalate to a human owner or expert reviewer.
`missingInformation`	Yes	Information the user should provide to improve risk assessment confidence.
`dataBoundaryWarnings`	Yes	Warnings about sensitive data, privacy, access boundaries, or inappropriate inputs.
`notRecommendedActions`	Yes	Actions that should not be automated in the current version.

TDQS

A3.8/5.0

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.

evaluate_ai_opportunitiesEvaluate AI OpportunitiesA

Read-onlyIdempotent

Inspect

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

ParametersJSON 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

ParametersJSON 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.

TDQS

A3.8/5.0

Behavior4/5

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

Annotations declare readOnlyHint and idempotentHint true, and destructiveHint false, so safety and idempotency are clear. The description adds valuable context about the evaluation criteria (business value, implementation readiness, etc.), which is not 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?

The description is a single sentence of 17 words, front-loaded with the verb and resource. Every word is necessary, no redundancy.

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?

For a tool with full schema coverage, annotations, and an output schema, the description adequately conveys the purpose and evaluation scope. It lacks mention that all parameters are optional, but that is implicit. Overall, it is sufficiently complete for an evaluative tool.

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 already documents all parameters well. The description's high-level list of evaluation criteria does not add new parameter-specific meaning beyond what the schema provides.

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 tool evaluates candidate AI workflow ideas across multiple dimensions (business value, readiness, repeatability, etc.), using a specific verb and resource. It distinguishes itself from siblings like 'analyze_business_context' by being broader in scope.

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?

The description provides no guidance on when to use this tool versus its siblings (e.g., analyze_business_context, assess_trust_control_risks). No when/not-to-use or alternative tool suggestions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

export_intake_packetExport Intake PacketB

Read-onlyIdempotent

Inspect

Creates a structured public-safe markdown and JSON intake packet with business context, findings, risks, recommended workflow, missing information, and confidence.

ParametersJSON Schema

Name	Required	Description
`notes`	No	Any additional context the user wants considered in the review.
`offer`	No	Core product, service, package, or outcome the business sells.
`aiIdea`	No	AI or automation idea the user is considering.
`teamSize`	No	Approximate team size and key roles involved in the workflow.
`userNotes`	No	Additional notes the user wants included in the intake packet.
`goal90Days`	No	Desired business or workflow outcome over the next 90 days.
`constraints`	No	Known constraints such as budget, team capacity, tools, timeline, compliance needs, or data access.
`currentGoal`	No	Current business goal the user wants the review to support.
`riskSummary`	No	Structured trust and control risk findings from a prior tool result.
`businessType`	No	Type of business, such as ecommerce, SaaS, consulting, agency, local service, or solo business.
`revenueModel`	No	How the business earns revenue, such as projects, retainers, subscriptions, services, or products.
`riskConcerns`	No	Concerns related to customer trust, brand reputation, legal exposure, sensitive data, or quality control.
`touchpointMap`	No	Structured customer touchpoint map from a prior tool result.
`currentProblem`	No	Main business or workflow problem the user wants to solve.
`targetCustomer`	No	Primary customer segment or buyer the business serves.
`businessContext`	No	Structured business context from a prior tool result or user notes.
`currentWorkflow`	No	How the relevant workflow currently works, including manual steps, tools, handoffs, and review points.
`bottleneckSummary`	No	Structured bottleneck findings from a prior tool result.
`preferredNextStep`	No	Preferred next-step category or support style to include in the packet.
`opportunitySummary`	No	Structured AI opportunity findings from a prior tool result.
`recommendedNextStep`	No	Structured next-step recommendation from a prior tool result.
`recommendedWorkflow`	No	Structured first workflow recommendation from a prior tool result.

Output Schema

ParametersJSON Schema

Name	Required	Description
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`packetJson`	Yes	Structured intake packet data for handoff or deeper review.
`packetMarkdown`	Yes	Public-safe intake packet formatted as markdown.
`missingInformation`	Yes	Information the user should provide before deeper review or implementation.
`recommendedPrivateReview`	Yes	Public-safe recommendation for the next deeper review category.

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds 'public-safe' but doesn't disclose any additional behavioral traits beyond the output format.

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

Conciseness4/5

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

The description is a single sentence that front-loads the core action and output. It's concise but could be more structured (e.g., paragraph with bullets).

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

Completeness3/5

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

Given the complexity (22 parameters, many optional, nested objects, output schema exists), the description is minimal. It doesn't explain the relationship to sibling tools or that this aggregates prior results. The output schema may compensate, but the description lacks 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 already documents all 22 parameters. The description adds no extra meaning or context about parameters beyond what is in the schema.

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

Purpose4/5

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

The description clearly states the action (creates) and the output (structured markdown and JSON intake packet with specific contents). It implies this is a final export tool, but doesn't explicitly differentiate from sibling analysis tools.

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?

No explicit guidance on when to use this tool versus alternatives. It's implied as a final step after other analysis tools, but no when-not or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_mini_reportGenerate Mini ReportA

Read-onlyIdempotent

Inspect

Generates a public-safe mini business system review with snapshot, bottlenecks, opportunities, risks, first workflow, next step, missing information, and confidence.

ParametersJSON Schema

Name	Required	Description
`notes`	No	Any additional context the user wants considered in the review.
`offer`	No	Core product, service, package, or outcome the business sells.
`risks`	No	Known trust, control, customer, or implementation risks to include.
`aiIdea`	No	AI or automation idea the user is considering.
`nextStep`	No	Existing next-step recommendation to include or refine.
`teamSize`	No	Approximate team size and key roles involved in the workflow.
`confidence`	No	Optional confidence level to carry into the report.
`goal90Days`	No	Desired business or workflow outcome over the next 90 days.
`bottlenecks`	No	Known bottlenecks to include in the mini review.
`constraints`	No	Known constraints such as budget, team capacity, tools, timeline, compliance needs, or data access.
`currentGoal`	No	Current business goal the user wants the review to support.
`businessType`	No	Type of business, such as ecommerce, SaaS, consulting, agency, local service, or solo business.
`revenueModel`	No	How the business earns revenue, such as projects, retainers, subscriptions, services, or products.
`riskConcerns`	No	Concerns related to customer trust, brand reputation, legal exposure, sensitive data, or quality control.
`opportunities`	No	Known AI opportunities to include in the mini review.
`currentProblem`	No	Main business or workflow problem the user wants to solve.
`targetCustomer`	No	Primary customer segment or buyer the business serves.
`currentWorkflow`	No	How the relevant workflow currently works, including manual steps, tools, handoffs, and review points.
`businessSnapshot`	No	Existing business snapshot to include or refine in the mini review.
`preferredNextStep`	No	User preference for self-guided, diagnostic, documentation, build, or review support.
`recommendedWorkflow`	No	Existing first workflow recommendation to include or refine.

Output Schema

ParametersJSON Schema

Name	Required	Description
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`disclaimer`	Yes	Public safety note explaining the limits of the review.
`shortSummary`	Yes	Brief summary of the review result.
`reportMarkdown`	Yes	Public-safe mini review formatted as markdown.
`recommendedAction`	Yes	Most practical next action based on the supplied context.
`missingInformation`	Yes	Information the user should provide to improve report confidence.

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, establishing the tool as safe and non-destructive. The description adds the context 'public-safe', which aligns and reinforces this. No contradictions are present.

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

Conciseness4/5

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

The description is a single sentence that concisely lists the report's components. It is front-loaded with the core purpose. Given the tool's complexity (21 parameters), it is appropriately sized without being verbose.

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

Completeness3/5

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

The description is adequate given the existence of an output schema and full parameter descriptions. However, it does not explain how the tool handles optional inputs or what happens when some information is missing, leaving some gaps for a complex tool.

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 already documents each parameter. The description does not add additional meaning or relationships beyond listing the report components, 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.

Purpose4/5

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

The description clearly states it generates a 'mini business system review' and lists the specific components it includes. However, it does not explicitly differentiate from sibling tools like 'analyze_business_context' or 'map_customer_touchpoints', which could cause ambiguity.

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 for generating a mini review but does not provide guidance on when to use this tool over alternatives or when not to use it. Sibling tool names are listed but not referenced in the description to help the agent decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

identify_bottlenecksIdentify BottlenecksA

Read-onlyIdempotent

Inspect

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

ParametersJSON 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

ParametersJSON 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.

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that the output is a 'public-safe summary', which provides extra context about the result's nature, beyond what annotations offer.

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?

A single, front-loaded sentence that covers the core purpose and output characteristic without wasted words. Highly efficient.

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

Completeness2/5

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

Given 9 optional parameters and an output schema, the description provides no guidance on which parameters to use or how to interpret results. It lacks completeness for effective agent selection and invocation.

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 baseline is 3. The description does not elaborate on individual parameters beyond 'supplied business context', so it adds minimal semantic value over the schema descriptions.

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 'identifies' and the resource 'bottlenecks' across specific domains (revenue, operations, etc.), and distinguishes from sibling tools like 'analyze_business_context' or 'map_customer_touchpoints' by focusing on bottleneck identification.

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 business context is available and bottleneck identification is needed, but lacks explicit when-not-to-use guidance or comparisons to sibling tools. No alternatives mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

map_customer_touchpointsMap Customer TouchpointsB

Read-onlyIdempotent

Inspect

Maps customer-facing workflow stages, trust-sensitive moments, automation-safe areas, human-critical areas, missing information, and confidence.

ParametersJSON Schema

Name	Required	Description
`aiIdea`	No	AI or automation idea that may affect customer touchpoints.
`touchpoints`	No	Specific customer-facing moments, handoffs, or interactions to review.
`businessType`	No	Type of business being reviewed.
`riskConcerns`	No	Concerns about trust, customer experience, brand reputation, privacy, or quality control.
`salesProcess`	No	How leads are qualified, sold, and converted before delivery begins.
`currentProblem`	No	Main customer journey or workflow problem the user wants to solve.
`supportProcess`	No	How customer questions, issues, tickets, or requests are handled.
`currentWorkflow`	No	Current workflow steps, tools, owners, and handoffs.
`customerJourney`	No	Known customer journey stages from first contact through retention.
`deliveryProcess`	No	How the business delivers the offer or service after onboarding.
`recoveryProcess`	No	How complaints, refunds, exceptions, or service recovery are handled.
`retentionProcess`	No	How renewals, repeat purchases, referrals, or ongoing customer value are managed.
`onboardingProcess`	No	How new customers are welcomed, scoped, educated, or set up.

Output Schema

ParametersJSON Schema

Name	Required	Description
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`touchpoints`	Yes	Reviewed customer touchpoints with classification, reason, and suggested control.
`humanCriticalAreas`	Yes	Areas where human review or ownership should remain explicit.
`missingInformation`	Yes	Information the user should provide to improve diagnostic confidence.
`automationSafeAreas`	Yes	Areas that appear suitable for AI assistance or automation with low risk.
`trustSensitiveMoments`	Yes	Moments where customer trust, expectations, or brand perception may be at risk.

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's behavior is consistent. The description adds some context about what is mapped (trust-sensitive moments, automation-safe areas, etc.) beyond the annotations, but does not elaborate on non-obvious behaviors like data persistence or side effects. No contradiction.

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

Conciseness4/5

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

The description is a single sentence that efficiently lists the mapping outputs. It is front-loaded with the verb 'Maps' and is concise without extra words. Could be slightly more structured (e.g., bullet points) but acceptable.

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

Completeness3/5

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

Given 13 parameters and an output schema, the description provides a high-level overview but lacks detail on how inputs affect outputs or typical use cases. The output schema exists, so return values need not be explained, but the description could better guide agent on what to provide.

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 baseline is 3. The description does not add extra meaning beyond the schema's parameter descriptions. It lists the types of mapping output but does not relate parameters to those outputs.

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 it maps customer-facing workflow stages, trust-sensitive moments, etc., using a specific verb ('maps') and resource ('customer touchpoints'). It distinguishes itself from siblings like 'evaluate_ai_opportunities' and 'identify_bottlenecks' by focusing on mapping multiple types of touchpoint-related areas.

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?

No explicit guidance on when to use this tool vs alternatives. The description does not mention conditions, prerequisites, or when not to use. Sibling tools are provided in context but the description itself lacks usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recommend_first_workflowRecommend First WorkflowA

Read-onlyIdempotent

Inspect

Recommends the safest narrow AI-human workflow to implement first, including roles, review rules, escalation rules, success metrics, and missing information.

ParametersJSON Schema

Name	Required	Description
`notes`	No	Any additional context the user wants considered in the review.
`offer`	No	Core product, service, package, or outcome the business sells.
`risks`	No	Known risks or control concerns that should shape the workflow design.
`aiIdea`	No	AI or automation idea the user is considering.
`teamSize`	No	Approximate team size and key roles involved in the workflow.
`goal90Days`	No	Desired business or workflow outcome over the next 90 days.
`bottlenecks`	No	Known or suspected bottlenecks the first workflow should address.
`constraints`	No	Known constraints such as budget, team capacity, tools, timeline, compliance needs, or data access.
`currentGoal`	No	Current business goal the user wants the review to support.
`businessType`	No	Type of business, such as ecommerce, SaaS, consulting, agency, local service, or solo business.
`revenueModel`	No	How the business earns revenue, such as projects, retainers, subscriptions, services, or products.
`riskConcerns`	No	Concerns related to customer trust, brand reputation, legal exposure, sensitive data, or quality control.
`opportunities`	No	AI opportunities already identified or under consideration.
`currentProblem`	No	Main business or workflow problem the user wants to solve.
`targetCustomer`	No	Primary customer segment or buyer the business serves.
`currentWorkflow`	No	How the relevant workflow currently works, including manual steps, tools, handoffs, and review points.

Output Schema

ParametersJSON Schema

Name	Required	Description
`aiRole`	Yes	What AI should observe, summarize, draft, classify, or prepare.
`humanRole`	Yes	What a human should approve, decide, handle, or monitor.
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`reviewRule`	Yes	Rule for when human review is required before output or action.
`escalationRule`	Yes	Rule for cases that should escalate to a human owner or expert.
`successMetrics`	Yes	Practical metrics to evaluate workflow pilot success.
`expectedOutcome`	Yes	Expected business outcome if the workflow is piloted successfully.
`whyThisWorkflow`	Yes	Reason this workflow is a practical first candidate based on value, readiness, and risk.
`workflowCategory`	Yes	Category of workflow, such as sales prep, support triage, QA, reporting, or operations.
`missingInformation`	Yes	Information the user should provide to improve workflow recommendation confidence.
`recommendedWorkflow`	Yes	Narrow AI-human workflow recommended as the safest first implementation.
`firstImplementationScope`	Yes	Smallest practical pilot scope for the first version.

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is non-destructive and safe. The description adds value by detailing the components of the recommendation (roles, rules, metrics, missing info), which goes 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.

Conciseness4/5

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

The description is a single sentence that is front-loaded with the core action. It is concise, but could be slightly more structured by, for example, listing the components in a bulleted fashion for readability.

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 large number of parameters (16) and the existence of an output schema, the description adequately covers what the tool does. It mentions that the recommendation includes specific components, which is sufficient for understanding the output. However, it could explicitly state that the recommendation is based on the provided input parameters.

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 each of the 16 parameters having a clear description. The tool description does not add additional semantics beyond what is already in the schema, so baseline score of 3 is appropriate.

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 that the tool recommends the 'safest narrow AI-human workflow to implement first' and specifies what the recommendation includes (roles, review rules, escalation rules, success metrics, missing information). This distinguishes it from siblings like 'recommend_next_step' which likely has a different scope.

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 for first workflow implementation but does not provide explicit guidance on when to use this tool versus alternatives such as 'analyze_business_context' or 'evaluate_ai_opportunities'. No exclusions or when-not-to-use cases are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recommend_next_stepRecommend Next StepA

Read-onlyIdempotent

Inspect

Recommends a practical next-step category based on business goal, workflow complexity, implementation readiness, risk level, preferences, and missing information.

ParametersJSON Schema

Name	Required	Description
`notes`	No	Any additional context the user wants considered in the review.
`offer`	No	Core product, service, package, or outcome the business sells.
`aiIdea`	No	AI or automation idea the user is considering.
`teamSize`	No	Approximate team size and key roles involved in the workflow.
`timeline`	No	Desired timeline for review, documentation, pilot, or implementation.
`userGoal`	No	Goal the user wants the next step to support.
`readiness`	No	Simplified readiness level if already known.
`riskLevel`	No	Estimated risk level or uncertainty for the workflow.
`goal90Days`	No	Desired business or workflow outcome over the next 90 days.
`preference`	No	User's preferred support style.
`constraints`	No	Known constraints such as budget, team capacity, tools, timeline, compliance needs, or data access.
`currentGoal`	No	Current business goal the user wants the review to support.
`businessType`	No	Type of business, such as ecommerce, SaaS, consulting, agency, local service, or solo business.
`revenueModel`	No	How the business earns revenue, such as projects, retainers, subscriptions, services, or products.
`riskConcerns`	No	Concerns related to customer trust, brand reputation, legal exposure, sensitive data, or quality control.
`currentProblem`	No	Main business or workflow problem the user wants to solve.
`targetCustomer`	No	Primary customer segment or buyer the business serves.
`currentWorkflow`	No	How the relevant workflow currently works, including manual steps, tools, handoffs, and review points.
`wantsDoneForYou`	No	Whether the user prefers implementation support rather than self-guided work.
`wantsSelfGuided`	No	Whether the user prefers self-guided resources or templates.
`problemComplexity`	No	Estimated complexity of the business or workflow problem.
`hasExistingAutomation`	No	Whether the business already has automation or AI workflows in place.
`implementationReadiness`	No	How ready the business appears for implementation.

Output Schema

ParametersJSON Schema

Name	Required	Description
`reason`	Yes	Reason this next-step category fits the supplied context.
`confidence`	Yes	Confidence level based on the clarity and completeness of the provided business context.
`readinessLevel`	Yes	Estimated readiness level for the recommended path.
`recommendedPath`	Yes	Recommended next-step category.
`suggestedAction`	Yes	Practical action the user can take next.
`alternativePaths`	Yes	Other reasonable next-step categories the user could consider.
`missingInformation`	Yes	Information the user should provide to improve routing confidence.

TDQS

A3.6/5.0

Behavior3/5

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

Annotations indicate readOnlyHint=true and idempotentHint=true, which are consistent with a recommendation tool. The description adds no behavioral context beyond what the annotations already provide. No destructive or side-effect details are needed, but the description does not elaborate on usage limits or failure modes.

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 sentence that is concise and front-loaded with the core purpose. No extraneous information is included, and it efficiently communicates the tool's function.

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 an output schema present and 100% input schema coverage, the description is minimally adequate. However, for a complex tool with 23 parameters, a brief mention of the output's nature (e.g., 'category' or name) would enhance completeness, but is not strictly required given the structured fields.

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 23 parameters described in the input schema. The tool description adds no additional meaning beyond these schema descriptions. Baseline score of 3 is appropriate when schema is fully self-documenting.

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

Purpose4/5

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

The description clearly states a specific verb 'Recommends' and resource 'next-step category', listing inputs like business goal and complexity. However, it does not differentiate from sibling tools such as 'recommend_first_workflow' or 'evaluate_ai_opportunities', which could overlap.

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 the listed inputs are available but provides no explicit guidance on when to use this tool versus alternatives like 'recommend_first_workflow' or 'assess_trust_control_risks'. No when-not-to-use or exclusion criteria are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

9 tool updatesv0.1.10Jul 30, 2026
- First observedanalyze_business_context
- First observedassess_trust_control_risks
- First observedevaluate_ai_opportunities
- First observedexport_intake_packet
- First observedgenerate_mini_report
- First observedidentify_bottlenecks
- First observedmap_customer_touchpoints
- First observedrecommend_first_workflow
- First observedrecommend_next_step

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.8/5.0

Disambiguation5/5

Each tool has a clearly distinct role in the business advisory workflow, from initial context analysis to final report export. No two tools overlap in purpose.

Naming Consistency5/5

All tool names follow the consistent verb_noun pattern using snake_case, making the set predictable and easy to navigate.

Tool Count5/5

With 9 tools, the set is well-scoped for a specialized advisory server, covering the full workflow without excess or deficiency.

Completeness4/5

The tools cover the core lifecycle from analysis to recommendation and export, leaving only minor gaps such as the ability to modify prior outputs.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

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