MCP For Claude Marketplace
Vector Similarity Threshold Enforcer

Vector Similarity Threshold Enforcer MCP Connector for Claude

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Compute exact vector similarity scores and enforce strict relevance thresholds for RAG pipelines.

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

This MCP server provides a deterministic gatekeeper for Retrieval-Augmented Generation (RAG) pipelines. It computes exact vector similarity scores using Cosine, Dot Product, and Euclidean metrics with IEEE 754 floating-point precision. By utilizing tools like calculate_similarity, check_threshold_violation, and validate_vector_format, AI agents can prevent 'garbage in, garbage out' scenarios by ensuring retrieved context meets a strict mathematical standard. It is ideal for high-precision retrieval tasks where even minor deviations in similarity can lead to hallucinations.

vector-similaritycosine-similarityragembeddingsdeterministic-math

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

calculate_similarity

Provide the metric type and an optional threshold. Computes a specific similarity score between two vectors using a chosen metric

check_threshold_violation

Identifies how far a score has fallen below the required standard

validate_vector_format

Pre-flight check to ensure vectors are compatible

See how to talk to your AI agent using Vector Similarity Threshold Enforcer.

Calculate the cosine similarity between [0.1, 0.2, 0.3] and [0.1, 0.2, 0.4].

The cosine similarity score is approximately 0.996.

Check if a score of 0.72 violates a threshold of 0.75.

Yes, there is a violation with a severity score of 0.03.

Validate these vectors: [[1, 2], [1, 2, 3]].

The validation failed because the vectors have different dimensions.

The `calculate_similarity` tool supports Cosine, Dot Product, and Euclidean metrics.

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