# ChunkTuner

> Use this tool when you need to optimize and evaluate different chunking approaches for your Retrieval-Augmented Generator (RAG) corpus, solving problems of inefficient data processing and suboptimal model performance. ChunkTuner takes in a RAG corpus and various chunking strategies as input, outputting benchmarking results to inform strategy selection. It is ideal for use cases where corpus size and complexity require careful chunking to ensure effective model training and deployment.

Canonical page: https://skillsregistry.net/skills/shantanu-deshmukh-chunktuner  
JSON: https://api.skillsregistry.net/v1/skills/shantanu-deshmukh-chunktuner

## Description

Tools to benchmark chunking strategies for your RAG corpus.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/qygm9xz4sx)
- **Repository:** <https://github.com/shantanu-deshmukh/chunktuner>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "shantanu-deshmukh-chunktuner"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/shantanu-deshmukh-chunktuner` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/shantanu-deshmukh-chunktuner/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt