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Local Model Suitability MCP

Official registry Productivity

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

Source code Website Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

Remote (streamable-http)
Claude Code 
claude mcp add --transport http local-model-suitability-mcp 'https://local-model-suitability-mcp-production.up.railway.app'
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "local-model-suitability-mcp": {
            "type": "http",
            "url": "https://local-model-suitability-mcp-production.up.railway.app"
        }
    }
}
NPM package local-model-suitability-mcp
Claude Code 
claude mcp add local-model-suitability-mcp -e 'ANTHROPIC_API_KEY=<your anthropic_api_key>' -- npx -y local-model-suitability-mcp@1.1.23
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "local-model-suitability-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "local-model-suitability-mcp@1.1.23"
            ],
            "env": {
                "ANTHROPIC_API_KEY": "<your anthropic_api_key>"
            }
        }
    }
}

Environment variables

  • ANTHROPIC_API_KEY required secret Anthropic API key for Claude routing analysis

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. Local Model Suitability MCP
  2. The Tool
  3. Data Sources
  4. Pricing
  5. Setup
  6. Harness Integration
  7. Legal

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:

FieldRequiredDescription
task✅The exact task you are about to send to a cloud model
quality_thresholdOptionalPRODUCTION (default) / PROTOTYPE / BEST_EFFORT
data_sensitivityOptionalPUBLIC (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

PlanCallsPrice
Free20/month$0
Starter500-call bundle$20
Pro2,000-call bundle$70

Subscribe at kordagencies.com

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.

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

Frequently asked questions

What is Local Model Suitability MCP?

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

How do I install Local Model Suitability MCP?

It runs as a remote server. In Claude Code: claude mcp add --transport http local-model-suitability-mcp 'https://local-model-suitability-mcp-production.up.railway.app'. For other clients, paste the JSON configuration from this page into their MCP settings.

Does Local Model Suitability MCP need an API key?

Yes. It expects ANTHROPIC_API_KEY, which you set in its configuration.

Is Local Model Suitability MCP open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains Local Model Suitability MCP?

It is published by OjasKord. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.