# Vector Similarity Threshold Enforcer MCP for AI Agents MCP > Vector Similarity Threshold Enforcer MCP acts as a mathematical gatekeeper for your RAG pipelines. It calculates exact similarity scores using Cosine, Dot Product, or Euclidean metrics and lets you set strict thresholds to block irrelevant context from reaching your agent. This prevents hallucinations by ensuring every piece of retrieved data meets a precise numerical standard before it is used in a prompt. ## Overview - **Category:** mathematics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/undefined/mcp - **Tags:** vector-similarity, cosine-similarity, rag, embeddings, deterministic-math ## Description Building RAG pipelines often feels like a guessing game where you hope the retrieved chunks are actually relevant. You tweak your chunk size, adjust your top-k, and pray the context doesn't contain garbage that confuses your agent. This MCP changes that by bringing deterministic math into the retrieval loop. Instead of relying on vague semantic overlaps, you can enforce hard limits on how much similarity is required for a piece of data to be considered valid. It works as a filter that sits between your vector database and your prompt construction. When you connect this through Vinkius, you're adding a layer of mathematical certainty to your retrieval process. You stop the 'garbage in, garbage out' cycle by automatically rejecting any context that falls below your specified similarity score. This means your agent only ever sees data that has passed a rigorous, measurable test. ## Tools ### calculate_similarity Computes a specific score between two vectors using your chosen metric. It provides the exact numerical overlap for any pair of embeddings. ### check_threshold_violation Identifies exactly how far a retrieved score falls below your required standard. This helps you quantify retrieval error margins. ### validate_vector_format Runs a pre-flight check to ensure all vectors are compatible before calculation. It prevents errors caused by dimension mismatches. ## Prompt Examples **Prompt:** ``` How close is this new document to my existing knowledge base? ``` **Response:** ``` I've analyzed the vector overlap. The similarity score is **0.94**, which indicates a very high level of relevance to your current dataset. ``` **Prompt:** ``` Is the retrieved context for this user query actually relevant? ``` **Response:** ``` Based on your threshold of 0.85, the current retrieval is **valid**. The calculated score is **0.89**. ``` **Prompt:** ``` Check if these two embeddings are compatible. ``` **Response:** ``` I've checked the dimensions. Both vectors are **1536-dimensional**, so they are perfectly compatible for comparison. ``` ## Capabilities ### Enforce strict relevance limits on retrieved context Automatically reject any vector retrieval that fails to meet your mathematical precision requirements. ### Verify vector dimensions before processing Run compatibility checks to ensure incoming embeddings match your expected model dimensions. ### Measure exact distance between embeddings Calculate precise similarity using Cosine, Dot Product, or Euclidean metrics. ### Detect threshold deviations Identify exactly how much a retrieved score falls below your required standard. ## Use Cases ### High-precision legal search An engineer uses `calculate_similarity` to ensure only highly relevant case law is passed to the agent, preventing incorrect legal advice. ### Medical documentation retrieval A developer uses `validate_vector_format` to ensure incoming clinical embeddings match the system's expected dimensions before processing. ### Customer support automation An ops lead implements `check_threshold_violation` to flag when retrieved help articles are too dissimilar from the user query to be useful. ### Financial report analysis A researcher uses the MCP to strictly enforce a 0.95 cosine similarity threshold for all retrieved quarterly earnings data. ## Benefits - Stop hallucinations by using similarity scores to reject low-quality context. - Prevent dimension mismatch errors with a quick compatibility check before processing. - Quantify retrieval quality by monitoring how much scores miss your target threshold. - Use Cosine, Dot Product, or Euclidean metrics to match your specific embedding model logic. - Automate the filtering of noisy data so your agent only sees verified information. ## How It Works The bottom line is you get a mathematically verified context window every single time. 1. Connect your vector database or embedding source to the MCP. 2. Define your required similarity threshold and preferred mathematical metric. 3. Your agent automatically filters out any retrieved vectors that fail the math check. ## Frequently Asked Questions **How does Vector Similarity Threshold Enforcer prevent hallucinations?** It acts as a filter that rejects any retrieved data that doesn't meet your mathematical similarity requirements, ensuring only high-quality context reaches your agent. **Can I use the Vector Similarity Threshold Enforcer with different embedding models?** Yes, as long as you can provide the vectors, this MCP can calculate scores using Cosine, Dot Product, or Euclidean metrics. **Does the Vector Similarity Threshold Enforcer work with Claude or Cursor?** Yes, any MCP-compatible client like Claude, Cursor, or Windsurf can use this to validate retrieval data. **How do I know if my retrieval threshold is too strict using Vector Similarity Threshold Enforcer?** You can monitor how often your scores fall below your target, allowing you to adjust the limit without breaking your pipeline. **Will the Vector Similarity Threshold Enforcer catch dimension mismatches?** Yes, it includes a check to ensure all vectors are formatted correctly before any math is performed. **What metrics are supported for similarity calculation?** The `calculate_similarity` tool supports Cosine, Dot Product, and Euclidean metrics. **How can I ensure my vectors are compatible before calculation?** You should use the `validate_vector_format` tool to verify that all arrays have identical dimensions and contain only valid numbers. **What happens if a similarity score is below my required threshold?** You can use `check_threshold_violation` to identify the exact severity of the violation and how far the score has fallen below your target.