# Tenkai2018 Ai Business System Advisor MCP server

Source: https://glimind.com/server/mcp-registry%2Fio.github.tenkai2018%2Fai-business-system-advisor-mcp
Registry id: `mcp-registry/io.github.tenkai2018/ai-business-system-advisor-mcp`
Status: live (checked 2026-10-26T20:48:51.860Z)

## Verdict

Tenkai2018 Ai Business System Advisor MCP server answered 100% of Glimind's liveness checks over 30 days, better than 42% of MCP servers, with 927 ms median handshake and 1 outage in 90 days. Its agent-readiness grade is A (100/100) across 9 tools, costing an agent about 5,811 tokens to load. Checked every few minutes; no tool is ever called.

## Measurements (30 days)

| Metric | Value | Better than |
|---|---|---|
| Uptime, 30 days | 100% | 42% of MCP servers |
| Median handshake latency | 927 ms | 26% of MCP servers |
| p95 handshake latency | 3029 ms | — |
| Checks in 30 days | 4,893 | — |
| Outages, 90 days | 1 | — |
| Median recovery | 6 min | — |
| Agent-readiness score | 100/100 (A) | 94% of MCP servers |
| Tools | 9 | — |
| Tool-list size for an agent | ~5,811 tokens | 36% of MCP servers |
| Days observed | 29 | — |

## Install

```json
{
  "mcpServers": {
    "tenkai2018-ai-business-system-advisor-mc": {
      "type": "http",
      "url": "https://mcp.prodxsolution.com/ai-business-system-advisor/mcp"
    }
  }
}
```

Claude Code:

```sh
claude mcp add --transport http tenkai2018-ai-business-system-advisor-mc https://mcp.prodxsolution.com/ai-business-system-advisor/mcp
```

## Tools (9)

- `analyze_business_context`: Summarizes the supplied business context, including customer, offer, workflow, goals, constraints, readiness signals, missing information, and confidence.
- `assess_trust_control_risks`: Reviews a proposed AI workflow for human review needs, data boundaries, quality controls, escalation triggers, unsafe automation risks, and confidence.
- `evaluate_ai_opportunities`: Evaluates candidate AI workflow ideas for business value, implementation readiness, repeatability, trust/control risk, warnings, and missing information.
- `export_intake_packet`: Creates a structured public-safe markdown and JSON intake packet with business context, findings, risks, recommended workflow, missing information, and confiden
- `generate_mini_report`: Generates a public-safe mini business system review with snapshot, bottlenecks, opportunities, risks, first workflow, next step, missing information, and confid
- `identify_bottlenecks`: Identifies likely revenue, operations, customer experience, and trust/control bottlenecks from the supplied business context and returns a public-safe summary.
- `map_customer_touchpoints`: Maps customer-facing workflow stages, trust-sensitive moments, automation-safe areas, human-critical areas, missing information, and confidence.
- `recommend_first_workflow`: Recommends the safest narrow AI-human workflow to implement first, including roles, review rules, escalation rules, success metrics, and missing information.
- `recommend_next_step`: Recommends a practical next-step category based on business goal, workflow complexity, implementation readiness, risk level, preferences, and missing informatio

Measured by Glimind (https://glimind.com): MCP handshake + tool list only, no tool is ever called. CC BY 4.0.