# Vector Index Recall Estimator MCP for AI Agents MCP

> Vector Index Recall Estimator lets you project how your vector search will actually perform before you spend money on infrastructure. It handles the math for HNSW and IVF algorithms, giving you clear trade-offs between speed, accuracy (recall), and memory footprint for your production database.

## Overview
- **Category:** analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/undefined/mcp
- **Tags:** vector-search, hnsw, ivf, ann, performance-estimation

## Description

Building a vector search system often feels like a guessing game. You're trying to balance how fast you want your results to appear against how accurate they need to be, all while watching your cloud bill climb. This MCP takes the guesswork out of that process. Instead of running expensive experiments on production-grade hardware just to see what happens, you can use this tool to model your results ahead of time. It lets you visualize the curves between recall and latency for different algorithms, so you can see exactly where the sweet spot lies for your specific dataset size and dimensions. It also handles the boring math of memory estimation, telling you how much RAM you'll actually need based on your vector count and precision tier. By integrating this into your workflow via the Vinkius catalog, you can move from I think this will work to I know this is the optimal configuration. It helps you pick the right parameters, like nprobe or efSearch, based on your target recall, ensuring your system stays performant as your data scales.

## Tools

### calculate_memory_usage
Predicts how much RAM your vector index will consume based on your data's scale and precision. Use this to size your cloud instances correctly.

### estimate_search_performance
Generates specific tradeoff points between search speed and recall for different algorithms. It helps you find the balance for your users.

### get_parameter_recommendations
Provides the ideal ranges for settings like efSearch and nprobe to hit your accuracy targets. This replaces manual trial and error.

## Prompt Examples

**Prompt:** 
```
How much memory will 5 million 512-dim vectors take in float32?
```

**Response:** 
```
For **5,000,000 vectors** with **512 dimensions** using **float32** precision:

*   **Estimated Memory:** ~9.2 GB
*   **Note:** This estimate covers the base index and can vary slightly depending on the specific algorithm (e.g., HNSW overhead).
```

**Prompt:** 
```
I need 90% recall for HNSW. What should my efSearch be?
```

**Response:** 
```
To achieve **90% recall** using an **HNSW index**, the recommended **efSearch** range is typically between **64 and 128**, depending on your dataset size.

*   **Lower bound (64):** Faster search, slightly lower recall.
*   **Upper bound (128):** Slower search, higher recall stability.
```

**Prompt:** 
```
Show me the trade-offs for an IVF index with 10 million vectors.
```

**Response:** 
```
Here are the estimated performance points for an **IVF index** with **10,000,000 vectors**:

| Recall | Est. Latency | Recommended nprobe |
| :--- | :--- | :--- |
| 80% | 1.2ms | 32 |
| 90% | 3.5ms | 64 |
| 95% | 7.8ms | 128 |
```

## Capabilities

### Visualize recall vs. latency curves
See the direct impact on speed when you increase your search accuracy requirements.

### Predict memory footprint
Get a clear estimate of the RAM required for your specific vector count and dimensions.

### Get optimal parameter ranges
Find the best nprobe and efSearch values to hit your target recall for HNSW and IVF.

### Model performance trade-offs
Compare different algorithms to see which one fits your specific latency constraints.

### Identify precision tier impacts
Determine how switching from float32 to float16 affects your hardware requirements.

## Use Cases

### Sizing a production cluster
An engineer needs to know if 10 million vectors will fit in a 32GB instance. They use calculate_memory_usage to confirm the requirements before provisioning.

### Finding the right nprobe value
A team wants 95% recall but doesn't know which nprobe value to set for their IVF index. They use get_parameter_recommendations to find the range.

### Comparing HNSW vs IVF
A startup needs to choose between HNSW and IVF for low-latency requirements. They use estimate_search_performance to see the tradeoff curves.

### Precision tier analysis
A data scientist wants to see how switching from float32 to float16 affects memory. They use calculate_memory_usage to compare the two options.

## Benefits

- Stop guessing your infrastructure costs by using calculate_memory_usage to see exact RAM needs before you buy.
- Avoid production bottlenecks by using estimate_search_performance to find the speed and accuracy balance.
- Save time on manual tuning with get_parameter_recommendations for HNSW and IVF settings.
- Make data-backed decisions on precision tiers to optimize your hardware spend and storage.
- Visualize the impact of scale on search latency before you actually ingest your data into production.

## How It Works

The bottom line is you get a data-backed blueprint for your vector database configuration before you deploy a single line of production code.

1. Provide your vector count, dimensions, and desired precision tier.
2. Select your target algorithm and your required recall percentage.
3. Receive a breakdown of memory usage and recommended parameter ranges.

## Frequently Asked Questions

**Can the Vector Index Recall Estimator help me save money on cloud costs?**
Yes. By using calculate_memory_usage, you can determine the exact RAM requirements for your data. This prevents you from over-provisioning expensive cloud instances.

**Does the Vector Index Recall Estimator support HNSW and IVF?**
Yes, it provides specific performance estimates and parameter recommendations for both HNSW and IVF algorithms.

**How accurate are the memory estimates?**
The estimates are based on standard vector precision tiers and algorithm overhead. They provide a reliable blueprint for hardware capacity planning.

**Can I use the Vector Index Recall Estimator to find the best nprobe value?**
Yes, you can ask for recommendations based on your target recall percentage and dataset scale to find the optimal nprobe range.

**What's the difference between HNSW and IVF performance?**
The tool allows you to compare both, showing you how HNSW handles recall-vs-latency trade-offs compared to the parameter-dependent scaling of IVF.

**Do I need to have my data ready to use the Vector Index Recall Estimator?**
No, you don't need your actual data. You only need to know your expected vector count, dimensions, and the precision tier you plan to use.

**What algorithms are supported?**
The server supports HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) algorithms.

**How can I estimate the RAM needed for my index?**
Use the `calculate_memory_usage` tool. You will need to provide the total vector count, dimensions per vector, and the precision tier (float32, float16, or int8).

**Can I get parameter suggestions for a specific recall target?**
Yes, the `get_parameter_recommendations` tool provides suggested ranges for parameters like `efSearch` or `nprobe` to help you reach your target recall percentage.