ai-business-system-advisor-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_business_contextB | Summarizes the supplied business context, including customer, offer, workflow, goals, constraints, readiness signals, missing information, and confidence. |
| map_customer_touchpointsC | Maps customer-facing workflow stages, trust-sensitive moments, automation-safe areas, human-critical areas, missing information, and confidence. |
| identify_bottlenecksA | Identifies likely revenue, operations, customer experience, and trust/control bottlenecks from the supplied business context and returns a public-safe summary. |
| evaluate_ai_opportunitiesA | Evaluates candidate AI workflow ideas for business value, implementation readiness, repeatability, trust/control risk, warnings, and missing information. |
| assess_trust_control_risksA | Reviews a proposed AI workflow for human review needs, data boundaries, quality controls, escalation triggers, unsafe automation risks, and confidence. |
| recommend_first_workflowA | Recommends the safest narrow AI-human workflow to implement first, including roles, review rules, escalation rules, success metrics, and missing information. |
| generate_mini_reportA | Generates a public-safe mini business system review with snapshot, bottlenecks, opportunities, risks, first workflow, next step, missing information, and confidence. |
| recommend_next_stepA | Recommends a practical next-step category based on business goal, workflow complexity, implementation readiness, risk level, preferences, and missing information. |
| export_intake_packetB | Creates a structured public-safe markdown and JSON intake packet with business context, findings, risks, recommended workflow, missing information, and confidence. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| run_mini_business_system_review | Guide the assistant to run a public-safe mini business system review. |
| evaluate_ai_workflow_idea | Evaluate whether an AI workflow idea is safe, useful, and reviewable. |
| prepare_diagnostic_intake | Prepare a structured intake packet for a deeper business-system review. |
| governance_gap_snapshot | Identify data boundaries, review rules, escalation rules, and quality controls. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| overview | Overview |
| how-it-works | How it works |
| readiness-guide | Readiness guide |
| sample-mini-report | Sample mini report |
| sample-intake-packet | Sample intake packet |
| privacy | Privacy note |
TDQS
Scored across 9 tools
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.
All tool names follow the consistent verb_noun pattern using snake_case, making the set predictable and easy to navigate.
With 9 tools, the set is well-scoped for a specialized advisory server, covering the full workflow without excess or deficiency.
The tools cover the core lifecycle from analysis to recommendation and export, leaving only minor gaps such as the ability to modify prior outputs.