MCP For Claude Marketplace
Vector Index Recall Estimator

Vector Index Recall Estimator MCP Connector for Claude

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Estimate ANN search performance, memory footprint, and optimal parameters.

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

This MCP server provides analytical tools to estimate the performance tradeoffs of Approximate Nearest Neighbor (ANN) vector indices. Use estimate_search_performance to generate recall-vs-latency tradeoff curves for HNSW and IVF algorithms. Calculate hardware requirements using calculate_memory_usage by providing vector count, dimensions, and precision tier. Additionally, use get_parameter_recommendations to find optimal parameter ranges like efSearch or nprobe for a target recall percentage based on your dataset scale.

vector-searchhnswivfannperformance-estimation

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

calculate_memory_usage

Estimates memory usage for a vector index

estimate_search_performance

Estimates search performance tradeoff points

get_parameter_recommendations

Suggests optimal parameters for a target recall

See how to talk to your AI agent using Vector Index Recall Estimator.

What is the estimated memory usage for 1 million vectors with 768 dimensions using float32 precision?

The estimated memory usage for 1,000,000 vectors of 768 dimensions at float32 precision is approximately 2.86 GB.

Show me the performance tradeoff for an HNSW index with m=16 and efSearch=100.

The estimated tradeoff points are: 85% recall at 2.5ms, 92% recall at 5.8ms, and 98% recall at 12.4ms.

What parameters should I use for an IVF index to achieve 95% recall with 10 million vectors?

To achieve 95% recall at a scale of 10,000,000 vectors using IVF, it is recommended to use an nprobe value between 64 and 128.

The server supports HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) algorithms.

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