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Local Model Suitability MCP

Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.

Open source Repository Open in the app JSON README (API)

About

Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.

Details

Kind
MCP servers
Topic
No topic detected
Publisher
ojaskord
Origin
official
Category
ferramentas
Transport
http
Version
1.1.23
Forks
1
Open pull requests
1
Last push
2026-08-24T07:49:06Z
Repository state
ativo
Language
JavaScript
License
MIT
Added
2026-08-29 03:02:08
Updated
2026-08-29 03:02:08
Origin id
io.github.OjasKord/local-model-suitability-mcp

README

[![smithery badge](https://smithery.ai/badge/OjasKord/local-model-suitability-mcp)](https://smithery.ai/servers/OjasKord/local-model-suitability-mcp)

# Local Model Suitability MCP

[![ToolRank](https://toolrank.dev/badge/dominant.svg)](https://toolrank.dev/ranking)

**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:**
```json
{
  "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 |

[Subscribe at kordagencies.com](https://kordagencies.com)

## Setup

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

### LangChain (Python)
```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)
```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](https://kordagencies.com/terms.html)

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