Local Model Suitability MCP

MCP serverProductivity

Your AI can check whether a task runs locally or needs cloud inference, so calls a local model can handle stop costing cloud money. This integration adds that check: give it a task and it tells you which option fits.

Use Local Model Suitability MCP in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add Local Model Suitability MCP and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use Local Model Suitability MCP to check local viability

Details

Available today. Use it from your connected AI after setup.

After adding it, ask your AI to check whether a task can run locally before sending it to the cloud. Tasks you run often are a good place to start looking for savings.

Local Model Suitability MCPStart free

What your AI can do with it

  • Check whether a task can run locally or needs the cloud
  • Identify calls that don't need cloud inference
  • Compare local and cloud options before a task runs
  • Cut spending on tasks a local model can handle

Install Local Model Suitability MCP

The server’s own address, for the clients that take one directly. Or connect Ahel once and every client you use reads it from one address, with the account kept on Ahel rather than in each client’s config.

  • Claude Code

    claude mcp add --transport http --scope user local-model-suitability-mcp 'https://local-model-suitability-mcp-production.up.railway.app'

    Run it once in your project, then open /mcp to approve any sign-in the server asks for.

  • Claude Desktop

    https://local-model-suitability-mcp-production.up.railway.app

    Add a custom connector in Settings, paste this address, and approve the sign-in.

  • Cursor

    cursor://anysphere.cursor-deeplink/mcp/install?name=local-model-suitability-mcp&config=eyJ1cmwiOiJodHRwczovL2xvY2FsLW1vZGVsLXN1aXRhYmlsaXR5LW1jcC1wcm9kdWN0aW9uLnVwLnJhaWx3YXkuYXBwIn0=

    Open the link and Cursor adds the server at that address.

  • ChatGPT

    https://local-model-suitability-mcp-production.up.railway.app

    In Settings, enable Developer mode, create an MCP app, and paste this address. Your plan and workspace must allow custom apps.

  • Codex

    codex mcp add local-model-suitability-mcp --url 'https://local-model-suitability-mcp-production.up.railway.app'

    Run it once, then sign in with codex mcp login local-model-suitability-mcp if the server asks for an account.

From the project's README

As published by ojaskord/local-model-suitability-mcp in README.md.

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.

Legal

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

Tools it offers (1)

What this server listed when Ahel dialed its public endpoint in Oct 2026, with no key and no account of yours. The names are the server’s own.

  • check_local_viability

Signals

Forks
1
Last commit
Sep 2026
Weekly downloads
84
Advanced
Delivery
local-model-suitability-mcp MCP server → your Ahel connector (mcp.ahel.ai) → your AI.
Item type
mcp-server
Key
io-github-ojaskord-local-model-suitability-mcp
Source
github.com/ojaskord/local-model-suitability-mcp
Hosted endpoint
https://local-model-suitability-mcp-production.up.railway.app