MCP For Claude Marketplace
Cost-Controlled Tool Selector

Cost-Controlled Tool Selector MCP Connector for Claude

A+

A deterministic engine to select the most cost-effective tool variant based on accuracy requirements.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a deterministic decision engine for optimizing LLM workflows. It evaluates multiple tool variants by balancing execution cost (ms) against accuracy requirements. By using the select_optimal_variant tool, agents can identify the most economical option that still meets their specific performance thresholds, ensuring efficient resource usage without sacrificing quality.

cost-optimizationdeterministicllm-efficiencytool-selectionaccuracy-threshold

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

compare_variant_efficiency

Evaluates the performance-to-cost ratio of two specific tool variants

list_qualified_variants

Answers which tools are capable of performing a task regardless of their cost

select_optimal_variant

Identifies the single best tool variant to use for a specific task based on efficiency and constraints

See how to talk to your AI agent using Cost-Controlled Tool Selector.

Find the best tool variant for a task requiring 0.85 accuracy from these options: [{'tool_name': 'fast_model', 'estimated_cost_ms': 100, 'estimated_accuracy': 0.80}, {'tool_name': 'balanced_model', 'estimated_cost_ms': 300, 'estimated_accuracy': 0.90}, {'tool_name': 'precise_model', 'estimated_cost_ms': 800, 'estimated_accuracy': 0.95}]

The selected variant is 'balanced_model' because it meets the 0.85 accuracy requirement with a cost of 300ms, which is lower than the 'precise_model' cost of 800ms.

Which tools are qualified if I need at least 0.7 accuracy? Variants: [{'tool_name': 'v1', 'estimated_cost_ms': 50, 'estimated_accuracy': 0.6}, {'tool_name': 'v2', 'estimated_cost_ms': 150, 'estimated_accuracy': 0.75}, {'tool_name': 'v3', 'estimated_cost_ms': 250, 'estimated_accuracy': 0.85}]

The qualified variants are 'v2' and 'v3'.

Compare the efficiency of 'cheap_tool' (cost: 50, accuracy: 0.5) and 'expensive_tool' (cost: 200, accuracy: 0.9).

The 'expensive_tool' is more efficient as it provides higher accuracy per unit of cost.

The engine first filters all variants to find those that meet the `min_accuracy` requirement. From that subset, it selects the variant with the lowest `estimated_cost_ms`. If costs are tied, it selects the one with higher accuracy.

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