CLAIR — Cascaded Lazy AI Routing

An MCP server that reduces token burn by lazily loading skills and tools only when needed, and routing repetitive subtasks to ML backends instead of the LLM.

Why CLAIR?

Loading all MCP tools and skill documents upfront can consume 5,000–15,000 tokens before the user's first message is processed. CLAIR introduces a 280-token always-on router that:

1. Classifies the incoming task

2. Returns only the skills and tools relevant to that task

3. Identifies subtasks that can bypass the LLM entirely via small ML models

Related MCP server: claw-tsaver

Proven Results — A/B Test

A controlled A/B test was run against a real Travel Planner web application across 50 tasks spanning 7 task categories (travel planning, UI changes, debugging, styling, state management, API development, button interactions).

Real LLM API calls (OpenRouter) confirmed the estimates:

• Control: 9,740 real prompt tokens per request

• CLAIR travel query: 328 real prompt tokens (−96.6% vs control)

• CLAIR dev task: 2,982 real prompt tokens (−69.4% vs control)

→ See the full report: demo/travel-planner/REPORT.md
→ Run the demo: demo/travel-planner/

Architecture

User Request → CLAIR Router (~280 tokens) → Domain Skill → Cascade Skill → LLM
                                           ↘ ML Backend (for repetitive tasks)

Installation

git clone https://github.com/concensure/clair-mcp-server.git
cd clair-mcp-server
npm install
npm run build
# Compiled output: dist/stdio.js (stdio transport) and dist/server.js (HTTP transport)

Compatibility — Works with Any MCP Client

CLAIR is client-agnostic. It works with any MCP-compatible AI coding assistant:

Kilo Code users: CLAIR works with Kilo Code + OpenRouter. Call clair_route before attaching skill/rule documents. CLAIR's output tells you exactly which documents to attach for the current task — compatible with Kilo Code's skill document attachment feature.

Rule documents: CLAIR's manifest can point to any markdown file — skill documents, Kilo Code .rules files, or custom instruction files. No modification required.

MCP tool savings: CLAIR also reduces MCP tool token costs. With 20 tools loaded upfront (~4,000–8,000 tokens), CLAIR routes which tools are needed per request, reducing tool context to ~800–1,200 tokens (75–90% savings). See RFC-CLAIR.md §8.3 for details.

Usage — stdio Transport (Universal)

stdio is the standard transport for local MCP servers across all major AI coding clients. After building, use dist/stdio.js as the entry point.

Kilo Code (mcp_settings.json)

{
  "mcpServers": {
    "clair": {
      "command": "node",
      "args": ["/path/to/clair-mcp-server/dist/stdio.js"],
      "alwaysAllow": ["clair_route", "clair_list_skills", "clair_offload"]
    }
  }
}

File location: %APPDATA%\Code\User\globalStorage\kilocode.kilo-code\settings\mcp_settings.json (Windows) or ~/.config/Code/User/globalStorage/kilocode.kilo-code/settings/mcp_settings.json (Linux/Mac)

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "clair": {
      "command": "node",
      "args": ["/path/to/clair-mcp-server/dist/stdio.js"]
    }
  }
}

Claude Code (CLI)

claude mcp add clair node /path/to/clair-mcp-server/dist/stdio.js

Cursor (.cursor/mcp.json)

{
  "mcpServers": {
    "clair": {
      "command": "node",
      "args": ["/path/to/clair-mcp-server/dist/stdio.js"]
    }
  }
}

OpenAI Codex / Any MCP-compatible client

{
  "mcpServers": {
    "clair": {
      "command": "node",
      "args": ["/path/to/clair-mcp-server/dist/stdio.js"]
    }
  }
}

Windows paths: Use double backslashes: "C:\\Users\\you\\clair-mcp-server\\dist\\stdio.js"

HTTP Transport (Remote / Team Deployment)

For remote deployment (Railway, Fly.io, Render), use dist/server.js which exposes an HTTP endpoint at /mcp. See HOSTING.md.

Available Tools

clair_route

Classifies a task and returns the minimal set of skills and MCP tools to load.

{
  "task_description": "Write a Python script to analyze sales data from a CSV",
  "prefer_ml_offload": true
}

Returns:

{
  "domains": ["coding", "data_analysis"],
  "load_skills": [
    { "id": "coding", "path": "skills/domains/coding.md", "token_cost": 420 },
    { "id": "data", "path": "skills/domains/data.md", "token_cost": 340 },
    { "id": "python", "path": "skills/cascades/coding/python.md", "token_cost": 320 }
  ],
  "load_tools": [
    { "id": "filesystem", "reason": "Required by coding skill" },
    { "id": "python_exec", "reason": "Required by data skill" }
  ],
  "ml_candidates": [],
  "estimated_tokens_saved": 1840,
  "routing_confidence": 0.85
}

clair_offload

Routes a repetitive subtask to an ML backend.

{
  "subtask_type": "sentiment_classification",
  "data": ["Great product!", "Terrible service", "It was okay"]
}

clair_list_skills

Lists all available skills, cascades, and ML backends with their token costs.

Skill Tree

skills/
├── router/ROUTER.md          ← always loaded (280 tokens)
├── domains/
│   ├── documents.md          (380 tokens)
│   ├── coding.md             (420 tokens)
│   ├── data.md               (340 tokens)
│   └── research.md           (290 tokens)
└── cascades/
    ├── documents/
    │   ├── docx.md           (560 tokens)
    │   ├── pdf.md            (480 tokens)
    │   └── pptx.md           (510 tokens)
    └── coding/
        ├── python.md         (320 tokens)
        ├── typescript.md     (310 tokens)
        └── testing.md        (280 tokens)

Total if loaded naively: ~4,170 tokens
Average CLAIR-routed load: ~1,040 tokens
Average savings: ~75%

ML Offload Registry

How Routing Works (Zero LLM Tokens)

CLAIR's routing is pure keyword matching — no LLM is invoked at routing time. The developer writes a manifest.json once with trigger keywords for each skill. At request time, CLAIR scans the user's query for those keywords in microseconds.

Token cost of routing: ~280 tokens (router overhead) + 0 LLM tokens
Token cost of NOT routing: 5,000–15,000 tokens (all skills loaded upfront)

→ See MANIFEST_GUIDE.md for how to write the manifest, organize the skill tree, and validate triggers — without consuming any LLM tokens.

Extending CLAIR

Adding a new skill

1. Create your skill markdown file in skills/domains/ or skills/cascades/

2. Add an entry to manifest.json

3. Include trigger keywords and token cost estimate

Adding an ML backend

Add an entry to the ml_offload_registry in manifest.json:

{
  "id": "your_task_id",
  "triggers": ["keyword1", "keyword2"],
  "volume_threshold": 5,
  "backend": "your-model-name",
  "backend_type": "huggingface|sklearn|spacy|rules|python_library",
  "latency_ms": 10,
  "accuracy": 0.90
}

Demo

The demo/travel-planner/ directory contains a complete A/B test demo:

• A Travel Planner web app with real LLM integration (OpenRouter)

• 15 skills across travel and software development domains

• Automated 50-task simulation script

• Full A/B test report with per-category breakdown

Hosting

Recommended: Railway, Fly.io, or Render for remote HTTP transport.
Local dev: stdio transport (default, no server needed).

See HOSTING.md for deployment guides.

RFC

The formal proposal for the CLAIR protocol is in RFC-CLAIR.md.

License

Apache 2.0 — see LICENSE.

Free to use commercially with attribution. See the licence comparison for details.