Context Optimizer MCP
by degenhero
The Context Optimizer MCP is a server that optimizes and extends context windows for large chat histories using Redis and in-memory caching. It acts as a middleware between your application and LLM providers, managing conversation context efficiently.
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What is Context Optimizer MCP?
Context Optimizer MCP is a server that uses Redis and in-memory caching to optimize and extend context windows for large chat histories, acting as a middleware for LLM providers like Anthropic.
How to use Context Optimizer MCP?
To use the server, you can install it using the MCP client, manually via git clone and npm, or using Docker. Configure the server by editing the .env file with your Anthropic API key and Redis settings. Then, send API requests to the server's endpoint, which is compatible with the standard Claude API, including a conversation_id for context tracking.
Key features of Context Optimizer MCP
Dual-Layer Caching
Smart Context Management
Rate Limiting
API Compatibility
Metrics Collection
Use cases of Context Optimizer MCP
Extending context windows for long conversations
Optimizing API costs by reducing token usage
Maintaining conversation history across multiple API calls
Improving LLM performance with relevant context
Building chat applications with large context requirements
FAQ from Context Optimizer MCP
What is the purpose of the Context Optimizer MCP?
What is the purpose of the Context Optimizer MCP?
It optimizes and extends context windows for large chat histories, improving LLM performance and reducing token usage.
Which LLM providers are supported?
Which LLM providers are supported?
Currently, it supports Anthropic's Claude models.
How does the caching mechanism work?
How does the caching mechanism work?
It uses a dual-layer caching system with an in-memory LRU cache for frequently accessed summaries and Redis for persistent storage.
How is context managed when conversations exceed token limits?
How is context managed when conversations exceed token limits?
Older messages are automatically summarized while preserving key information.
How can I track context across multiple API calls?
How can I track context across multiple API calls?
By providing a consistent conversation_id in requests, the server can maintain context.