MCP For Claude Marketplace
Dynamic Context Window Packer

Dynamic Context Window Packer MCP Connector for Claude

A+

Optimally packs retrieved documents into LLM context windows using a greedy knapsack algorithm.

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

Solve the context overflow problem in RAG systems. This MCP server provides tools to select the most valuable information that fits within a finite LLM window. By using the pack_context tool, you can prioritize documents based on their informational density (relevance score divided by token count). It also includes validate_token_capacity to ensure your document sets fit within limits and calculate_density_metrics to analyze the efficiency of your retrieved data. This is essential for preventing errors in LangChain or CrewAI workflows.

ragcontext-windowknapsack-algorithmtoken-managementllm-efficiency

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

calculate_density_metrics

Provide insight into the informational density of the provided document set

pack_context

Select the optimal set of documents to fit within a specific LLM context window

validate_token_capacity

Verify if a proposed set of documents and a system prompt can physically fit within a given window

See how to talk to your AI agent using Dynamic Context Window Packer.

Pack these documents into a 4000 token window with a 500 token reservation: [{id: 'doc1', tokens: 2000, relevance: 10}, {id: 'doc2', tokens: 2500, relevance: 15}, {id: 'doc3', tokens: 500, relevance: 8}]

The selected documents are doc3 (500 tokens) and doc1 (2000 tokens), totaling 2500 tokens used. Doc2 was dropped because it would exceed the 3500 token effective budget.

Check if 1500 tokens of documents can fit in a 2000 token window with a 600 token reservation.

No, the documents cannot fit. The total requirement is 2100 tokens, which exceeds the 2000 token capacity by a deficit of 100 tokens.

What is the average density of these documents: [{tokens: 100, relevance: 50}, {tokens: 200, relevance: 20}]?

The average density is 0.3. The first document has a density of 0.5 and the second has a density of 0.1.

It uses a deterministic greedy knapsack strategy. It calculates the density (relevance score / token count) for each document and selects them in descending order of density until the effective budget is exhausted.

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