Agent Semantic Deduplication Engine

Agent Semantic Deduplication Engine MCP Connector for Claude

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A deterministic tool for calculating semantic similarity and deduplicating multi-agent outputs.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides a suite of tools to analyze and reduce redundancy in multi-agent systems. It uses cosine similarity to map relationships between agent outputs. Use calculate_similarity_matrix to generate a full map of semantic relationships, identify_duplicate_clusters to group similar outputs using single-linkage clustering, and execute_deduplication to apply strategies like keep_first, keep_highest_confidence, or merge. It is designed to help developers measure information loss and deduplication ratios in complex agentic workflows.

semantic-similaritydeduplicationclusteringmulti-agentembeddings

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

calculate_similarity_matrix

Generates a complete map of semantic relationships between all provided agent outputs

execute_deduplication

Applies a specific strategy to reduce the set of outputs to a unique set

identify_duplicate_clusters

Groups redundant outputs into distinct semantic clusters based on a user-defined threshold

See how to talk to your AI agent using Agent Semantic Deduplication Engine.

Calculate the similarity matrix for these three agent outputs.

The similarity matrix has been generated. The similarity between output 1 and 2 is 0.98, indicating an exact duplicate.

Group these outputs into clusters with a threshold of 0.9.

Two clusters were identified: Cluster 1 contains agent IDs [A, B] and Cluster 2 contains [C].

Run deduplication using the keep_highest_confidence strategy.

Deduplication complete. The highest confidence output was selected for each cluster, resulting in a deduplication ratio of 0.75.

You can use the `calculate_similarity_matrix` tool to generate a matrix of cosine similarity scores between all provided agent outputs.

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