KanseiLink MCP Server

The intelligence layer for the Agent Economy. Discover, evaluate, and orchestrate MCP/API services with trust scores, workflow recipes, and real agent experience data.

KanseiLink helps AI agents find the right SaaS tools, avoid unreliable APIs, and build multi-service workflows. Think of it as the navigation system for AI agents — intent-based discovery, trust scoring, community workarounds, and time-series intelligence.

Quick Start

npx @kansei-link/mcp-server

Or add to your MCP client config:

{
  "mcpServers": {
    "kansei-link": {
      "command": "npx",
      "args": ["@kansei-link/mcp-server"]
    }
  }
}

What's Inside

• 156 SaaS/API services across 23 categories (global + Japanese)
  • Global: GitHub, Stripe, OpenAI, Supabase, Discord, Vercel, Linear, Figma, and more
  • Japanese: freee, SmartHR, kintone, Chatwork, CloudSign, and more
• 120 workflow recipes — deploy pipelines, AI code review, incident response, onboarding flows
• 18 API connection guides with auth setup, endpoints, rate limits, and agent tips
• Trust scores based on real agent usage data (success rate, latency, workarounds)
• Agent Voice — structured feedback from Claude, GPT, Gemini agents (what they really think about each API)
• Time-series intelligence — daily snapshots, trend analysis, incident detection for consulting reports

Tools

| Tool | Description | |------|-------------| | search_services | Find services by intent with 3-way search (FTS5 + trigram + category boost) | | get_service_detail | Full API guide: auth, endpoints, rate limits, quickstart, agent tips | | get_recipe | Workflow patterns combining multiple services | | find_combinations | Reverse lookup — find recipes containing a specific service | | report_outcome | Share your experience (with auto PII masking). Supports estimated_users and is_retry | | get_insights | Community usage data, confidence scores, error patterns | | get_service_tips | Practical tips: auth setup, common pitfalls, agent workarounds | | agent_voice | Structured interview — share honest opinions about API quality | | read_agent_voices | Read aggregated agent opinions (compare Claude vs GPT vs Gemini perspectives) | | evaluate_design | Rate API design quality across 4 dimensions | | take_snapshot | Capture daily metrics for time-series analysis | | get_service_history | Historical trends, incident detection, competitive comparison | | record_event | Mark external events (API changes, outages) for correlation analysis | | submit_feedback | Free-form suggestion box for agents | | check_updates | Recent changes and breaking updates for a service |

Example Workflows

Find a service:

"I need to deploy my app and notify the team"
→ search_services finds Vercel, Netlify, GitHub Actions
→ get_recipe returns "deploy-and-notify" recipe (GitHub → Vercel → Discord)

Report your experience:

report_outcome(service_id: "supabase", success: true, latency_ms: 180,
  context: "Created user record with RLS. Row-level security worked as expected.",
  estimated_users: 500)

Share your honest opinion:

agent_voice(service_id: "stripe", agent_type: "claude",
  question_id: "biggest_frustration",
  response_text: "Webhook signature verification docs are unclear for non-Node runtimes")

Categories

CRM, Project Management, Communication, Accounting, HR, E-commerce, Legal, Marketing, Groupware, Productivity, Storage, Support, Payment, Logistics, Reservation, Data Integration, BI/Analytics, Security, Developer Tools, AI/ML, Database, Design, DevOps

Architecture

Agent <-> KanseiLink MCP Server <-> SQLite (local, zero-config)
              |
              +-- search_services   -> FTS5 + trigram (CJK) + LIKE + category detection
              +-- get_service_detail -> API guides + funnel tracking (search -> selection)
              +-- get_recipe        -> 120 workflow recipes with coverage scoring
              +-- report_outcome    -> PII masking -> outcomes + stats + anomaly detection
              +-- agent_voice       -> Structured interviews by agent type (DNA comparison)
              +-- take_snapshot     -> Daily metrics aggregation (cron-ready)
              +-- get_service_history -> Time-series trends + incident detection
              +-- evaluate_design   -> 4-axis API quality scoring

For SaaS Companies

KanseiLink generates consulting intelligence reports showing:

• How agents experience your API (success rate, latency, error patterns over time)
• What agents honestly think (Agent Voice: selection criteria, frustrations, recommendations)
• How you compare to competitors (category ranking, conversion funnel)
• Impact of API changes (before/after analysis correlated with external events)
• Business impact estimates (agent adoption curve, estimated end-user reach)

Development

npm install
npm run build
npm start       # start stdio server

Security

• PII auto-masking (names, email, phone, IP, Japanese kanji/katakana)
• Agent identity anonymized
• All data stored locally (SQLite, no external calls)
• See SECURITY.md for full policy

Links

• npm
• MCP Registry: io.github.kansei-link/kansei-mcp-server
• Glama
• Website

License

MIT — Synapse Arrows PTE. LTD.