Summary
This is a cost optimization layer that sits between your agent and cloud LLM APIs. It exposes a single tool, check_local_viability, that evaluates whether a task can run on Ollama, LM Studio, or llama.cpp instead of paying for Claude or GPT calls. Pass it your prompt and quality threshold, and it returns a verdict with confidence level, recommended local models, and estimated savings. Useful if you're running high volumes of mixed complexity tasks and want to automatically route simple summarization or classification work to local 7B models while reserving cloud calls for complex reasoning. The routing logic itself runs on Claude Sonnet. Free tier gives you 20 decisions per month tracked by IP.
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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:
| Field | Required | Description |
|---|---|---|
task | ✅ | The exact task you are about to send to a cloud model |
quality_threshold | Optional | PRODUCTION (default) / PROTOTYPE / BEST_EFFORT |
data_sensitivity | Optional | PUBLIC (default) / INTERNAL / CONFIDENTIAL |
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
| Plan | Calls | Price |
|---|---|---|
| Free | 20/month | $0 |
| Starter | 500-call bundle | $20 |
| Pro | 2,000-call bundle | $70 |
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
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