MCP For Claude Marketplace
Agent Response Cache Calculator

Agent Response Cache Calculator MCP Connector for Claude

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A deterministic simulation engine to evaluate and optimize cache performance, TTL settings, and eviction strategies for AI agent responses.

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

Optimize your AI agent's response storage with precision. This MCP server provides a deterministic simulation engine to evaluate how different cache configurations impact performance. Use simulate_cache_performance to derive hit ratios, miss penalties, and eviction rates under various scenarios. You can also determine the ideal expiration window using calculate_optimal_ttl or project resource requirements with estimate_memory_footprint. It helps identify risks like cache stampedes and stale data probability, ensuring your agent's memory is both efficient and reliable.

cachesimulationoptimizationai-agentsperformance

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

calculate_optimal_ttl

Determines the ideal TTL setting based on the distribution of response validity durations

estimate_memory_footprint

Calculates the projected memory consumption of the cache

simulate_cache_performance

Executes a full simulation of a request pattern against specific cache constraints to derive core performance metrics

See how to talk to your AI agent using Agent Response Cache Calculator.

Simulate a cache with 100 entries and a 60s TTL using LRU policy for these requests: [{"query_hash": "a1", "frequency": 50, "response_time_ms": 200}, {"query_hash": "b2", "frequency": 10, "response_time_ms": 500}]

The simulation results show a hit ratio of 0.83 with a miss penalty of 2.5. The cache size efficiency is 0.02, and no high stale probability was detected.

What is the optimal TTL for these response durations: [10.5, 20.0, 15.2, 100.0, 45.5]?

The optimal TTL is 45.5 seconds.

Calculate the memory usage for a cache of 500 entries where each entry is 2KB.

The total estimated memory usage is 1024000.0 bytes.

The results are deterministic. By using `simulate_cache_performance`, you receive exact calculations for hit ratios and eviction rates based on the specific request patterns provided.

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