Local Model Suitability MCP

Cloud inference is expensive. Everything that can run locally should.

This MCP server tells your agent — before every cloud API call — whether the task can be handled by a local model instead. Route to Ollama, LM Studio, or llama.cpp when you can. Only pay for cloud when you must.

The Tool

check_local_viability

Call this BEFORE every cloud inference call. If verdict is LOCAL, skip the cloud call entirely and route to your local model. Only use cloud when this tool returns CLOUD.

Inputs:

CONFIDENTIAL forces LOCAL regardless of task complexity — data never leaves the machine.

Response:

{
  "verdict": "LOCAL",
  "confidence": "HIGH",
  "reason": "Simple text summarisation — no reasoning depth required. Any 7B+ local model handles this well.",
  "estimated_cost_saving": "$0.002-0.008 saved per call at claude-sonnet pricing",
  "recommended_local_models": ["llama3.2:8b", "mistral-7b", "phi3:medium"],
  "cloud_justified_reason": null,
  "analysis_type": "AI-powered cost routing — NOT a simple lookup"
}

Data Sources

• AI reasoning: Anthropic Claude (claude-sonnet) — cost routing analysis
• No external data sources — pure AI reasoning

Pricing

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Setup

{
  "mcpServers": {
    "local-model-suitability": {
      "command": "npx",
      "args": ["-y", "local-model-suitability-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "your-key",
        "API_KEY": "your-lms-api-key-for-paid-tier"
      }
    }
  }
}

Free tier requires no API key — tracked by IP.

Harness Integration

Claude Code / Claude Desktop (.mcp.json)

{
  "mcpServers": {
    "local-model-suitability": {
      "type": "http",
      "url": "https://local-model-suitability-mcp-production.up.railway.app"
    }
  }
}

LangChain (Python)

from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
    "local-model-suitability": {
        "url": "https://local-model-suitability-mcp-production.up.railway.app",
        "transport": "http"
    }
})
tools = await client.get_tools()

OpenAI Agents SDK (Python)

from agents import Agent, HostedMCPTool
agent = Agent(
    name="Assistant",
    tools=[HostedMCPTool(tool_config={
        "type": "mcp",
        "server_label": "local-model-suitability",
        "server_url": "https://local-model-suitability-mcp-production.up.railway.app",
        "require_approval": "never"
    })]
)

LangGraph

Same as LangChain above — langchain-mcp-adapters works with LangGraph natively.

Legal

Results are for cost-optimisation guidance only and do not constitute technical advice. Full terms: kordagencies.com/terms.html