Model Routing Optimizer

Model Routing Optimizer MCP Connector for Claude

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A deterministic engine for selecting the optimal LLM based on cost, quality, and latency.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides a deterministic decision engine to select the best Large Language Model (LLM) for any given task. By analyzing available models against specific requirements, it uses strategies like quality_first, cost_first, or balanced to find the ideal match. You can use route_request to determine the best model for a specific complexity level, get_model_catalog to view supported models, and calculate_batch_accuracy to evaluate historical performance. It automatically flags potential issues like INSUFFICIENT_QUALITY or SLA_VIOLATION based on your defined thresholds.

llmroutingcost-optimizationquality-assuranceautomation

3 tools expose this connector's capabilities to your AI agent.

calculate_batch_accuracy

Evaluates the historical performance of the routing engine

get_model_catalog

Retrieves the static list of models currently supported by the optimizer

route_request

Determines the single best model to handle a specific task based on the selected strategy

See how to talk to your AI agent using Model Routing Optimizer.

Find the best model for a task with complexity 8 using the balanced strategy.

The selected model is GPT-4o with an expected cost of $0.015 and an expected latency of 2.5 seconds.

Which model should I use if I want the lowest possible cost for a simple task?

The model selected using the cost_first strategy is Haiku with a cost of $0.0002 per token.

Check the current list of available models.

The current catalog includes Elite Tier models like GPT-4o, Efficient Tier models like Claude 3.5 Sonnet, and Economy Tier models like Llama 3.

The engine uses three strategies: `quality_first` picks the highest quality model, `cost_first` picks the cheapest, and `balanced` picks the best quality-to-cost ratio.

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