MCP For Claude Marketplace
Context Redundancy Deduplicator

Context Redundancy Deduplicator MCP Connector for Claude

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Identify and quantify exact N-gram overlaps across RAG documents to optimize context window usage.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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The Context Redundancy Deduplicator MCP server provides a deterministic engine for identifying and quantifying exact N-gram overlaps across multiple retrieved RAG documents. By utilizing exact string hashing, it computes redundancy percentages, flags documents exceeding a 70% overlap threshold, and calculates the precise byte-size savings achievable by removing duplicate text blocks. This is essential for optimizing context window efficiency in large-scale retrieval pipelines.

n-gramdeduplicationcontext-windowrag-optimizationtext-analysis

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

find_duplicate_segments

Isolate and identify the specific text blocks that are identical across the provided documents

analyze_redundancy

Perform a comprehensive redundancy analysis across a set of documents using a specific N-gram size

calculate_savings_projection

Estimate the impact of deduplication on context window limits or storage

See how to talk to your AI agent using Context Redundancy Deduplicator.

Analyze these three documents for redundancy using 5-grams: ['Doc A content', 'Doc B content with overlap', 'Doc C content'].

The analysis shows a redundancy percentage of 12.5%, with no documents flagged as high-risk outliers.

How much space can I save if my original dataset is 5000 bytes and the redundant size is 1200 bytes?

The efficiency gain is a 24% reduction, resulting in a new estimated dataset size of 3800 bytes.

Find all repeating patterns in this text: 'The quick brown fox jumps over the lazy dog. The quick brown fox is fast.'

Identified duplicate sequences include: 'The quick brown fox'.

An N-gram is a contiguous sequence of N items (characters or words) from a given sample of text used to identify repetitions.

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