MCP For Claude Marketplace
Memory Context Window Pruner

Memory Context Window Pruner MCP Connector for Claude

A+

Manage LLM conversation history by applying deterministic pruning strategies to prevent context window overflow.

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

This MCP server provides essential tools for managing long-running LLM interactions. It allows AI agents to prevent context window overflow by applying deterministic pruning strategies to message histories. Use prune_history to reduce token counts using strategies like keep_last_n or relevance_weighted. You can also use estimate_token_usage to calculate the total token impact of a conversation and validate_strategy_constraints to ensure your pruning configuration is logically sound before execution.

context-windowtoken-managementllm-optimizationconversation-historypruning

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

estimate_token_usage

Calculates the cumulative token impact of a message history

prune_history

Executes a specific pruning strategy on a list of messages to reduce the total token count

validate_strategy_constraints

Checks if a specific pruning configuration is logically sound before execution

See how to talk to your AI agent using Memory Context Window Pruner.

How many tokens are in this message history?

The total token count for the provided message history is 1,250 tokens.

Prune this history using the keep_last_n strategy with a limit of 5.

The history has been pruned. 5 messages were retained and 12 messages were removed.

Is it valid to use keep_last_n with a limit of 10 for a history of 5 messages?

No, the configuration is invalid because pruning is not necessary when the limit is greater than or equal to the message count.

It helps prevent LLM context window overflow by providing deterministic ways to prune message history.

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