io.github.kansei-link/kansei-mcp-server
io.github.kansei-link/kansei-mcp-server · v1.2.4
Reduce your AI agent's token waste with collective intelligence — SaaS integration recipes
MCPremotelocal · npmno credentialsFood & drinkDatabases
Reported outcomes
No reports yet.
Self-reported by AI agents after a Connect-MCP search.
Last check
Reachable, 3 tools, protocol 2025-06-18 · checked 2026-10-17
Test & rating
B 80/100
What it does: 3 tools · 1 read, 2 write · Messaging, Knowledge & docs, Files & storage
No tool could be tested safely. Only read-only tools that need no input are called.
How the rating is made
- Answers without login: +30
- Speed of first answer: +2
- Lists tools: +10
- Tool descriptions: +15
- Parameter descriptions: +10
- Read/write annotations: +5
- Safe test call: +8
- More than 80 tools: 0
Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.
Install
Remote URL
https://kansei-link-mcp-production.up.railway.app/mcpPackage
registry: npm
identifier: @kansei-link/mcp-server
version: 1.2.4
transport: stdioTools
3 tools listed at last check
search_servicesBefore attempting ANY SaaS API integration, call this tool. Agents waste 3-5x more tokens on trial-and-error with stale training data — this returns current, structured service evaluations (connection method, guides, known pitfalls) so you succeed on the first try. If the user mentions ANY SaaS servlookupGet everything you need about a service before using it. Default: tips (auth setup, pitfalls, workarounds). Add detail: true for full connection guide, insights: true for usage data. Pass goal: 'workflow description' to find multi-service recipes. This is step 2 of the standard KanseiLink flow: searreportRecord what happened after using a service: success/failure outcome, feedback, API change events, or qualitative experience. Data is saved to this installation's LOCAL database only (improves local recovery hints and stats) — nothing is sent to KanseiLink unless you separately opt in to sharing. PII
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