Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.
Local Model Suitability MCP (MCP Server)
This MCP server checks whether a task can run locally instead of using cloud inference. It is designed to decide, before each cloud API call, whether routing to a local model is viable to avoid unnecessary cloud usage. It supports local routing through Ollama, LM Studio, or llama.cpp.
🛠️ Key Features
Determines local vs cloud viability prior to cloud API calls
Routes eligible tasks to local inference backends
Includes a single tool: check_local_viability
🚀 Use Cases
Cost optimization by avoiding cloud inference when local execution is sufficient
Model routing decisions for agents before making cloud calls
Local deployment to support privacy-focused workflows
⚡ Developer Benefits
Reduces cloud spend by preventing calls that don’t need cloud inference
Simplifies agent decision-making around local-llm execution
Compatible with MCP server workflows (mcp-server)
⚠️ Limitations
Only described tool is check_local_viability; no additional tools are listed in the provided data
Checks whether a task can be handled by a local model instead of a cloud API. Call this BEFORE your agent routes any task to a cloud model — at the moment a task is ready to be sent and the model has not yet been selected. Use this when your agent is about to make a cloud inference call and wants to determine whether a local model could handle it at lower cost. Returns LOCAL/CLOUD/EITHER verdict with recommended model. If verdict is LOCAL, skip the cloud call entirely — saves cost on every routable inference call. Pass data_sensitivity=CONFIDENTIAL to force LOCAL verdict for tasks that must not leave the machine. Do not route to cloud without checking local viability first.
Parameters3
task
string
required
The exact task you are about to send to a cloud model. Be specific — include what the input is and what output you need.
quality_threshold
string
optional
PRODUCTION = output quality matters and errors are costly. PROTOTYPE = approximate results acceptable. BEST_EFFORT = speed and cost trump quality. Defaults to PRODUCTION.
data_sensitivity
string
optional
CONFIDENTIAL forces LOCAL verdict regardless of task complexity — data must not leave the machine. Defaults to PUBLIC.
Raw schema
{
"type": "object",
"properties": {
"task": {
"type": "string",
"description": "The exact task you are about to send to a cloud model. Be specific — include what the input is and what output you need."
},
"quality_threshold": {
"type": "string",
"enum": [
"PRODUCTION",
"PROTOTYPE",
"BEST_EFFORT"
],
"description": "PRODUCTION = output quality matters and errors are costly. PROTOTYPE = approximate results acceptable. BEST_EFFORT = speed and cost trump quality. Defaults to PRODUCTION."
},
"data_sensitivity": {
"type": "string",
"enum": [
"PUBLIC",
"INTERNAL",
"CONFIDENTIAL"
],
"description": "CONFIDENTIAL forces LOCAL verdict regardless of task complexity — data must not leave the machine. Defaults to PUBLIC."
}
},
"required": [
"task"
]
}
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
