Memory Cache MCP Server

The Ibproduct Ib Mcp Cache Server is a lightweight, token‑saving MCP service that sits between your AI client and any language model. By caching data locally, it eliminates the need to repeatedly transmit large payloads—such as file contents or computed results—across the network, thereby cutting token usage and improving response times. This is especially valuable for developers who frequently query the same resources or perform repetitive calculations in their AI workflows.

At its core, the server intercepts data requests from an MCP client and stores them in an in‑memory cache keyed by the request parameters. When the same request is issued again, the server returns the cached result instead of forwarding the original payload to the model. Because the token count is tied directly to the amount of data sent, this mechanism can reduce token consumption by orders of magnitude for workloads that involve repeated file reads or heavy computations.

Key capabilities include:

• Configurable cache limits – Set maximum entries, memory usage, and default TTL to balance performance with resource constraints.
• Automatic eviction policies – Least‑recently‑used items are purged when limits are exceeded, and expired entries are cleaned up on a configurable interval.
• Transparent integration – Works with any MCP client and any model that tokenizes inputs; no changes to the client code are required.
• Real‑time statistics – Periodic updates of hit/miss rates help developers monitor cache effectiveness and fine‑tune settings.

Typical use cases span from data‑driven research (repeatedly querying the same dataset), to interactive debugging tools that re‑evaluate code snippets, and even chatbot backends that reuse common knowledge bases. In each scenario, the cache removes redundant data transfer, lowers latency, and keeps token budgets within limits.

By embedding this cache server into an AI workflow, developers gain a powerful, low‑overhead optimization that seamlessly scales with the complexity of their models and the frequency of their data accesses.