AI Tools
Score candidate RAG chunk-splitting schemes for a document across four metrics — coherence (clean sentence/paragraph boundaries), coverage (topic focus / key-term concentration), context overlap health, and chunk-size consistency — then compare up to three schemes side by side and recommend the best. Pure offline heuristics, no model calls.
Call this tool from your code in three languages.
curl -X POST 'https://api.elysiatools.com/en/api/tools/ai-rag-chunk-quality-scorer' \
-H 'Content-Type: application/json' \
-d '{"document":"Retrieval-augmented generation combines a retriever and a language model. The retriever finds relevant passages in a knowledge base. Chunking strategy strongly affects retrieval quality. Smaller chunks are precise but lose context. Larger chunks carry context but dilute relevance. Overlap between chunks preserves context across boundaries. Evaluate several schemes before indexing.","unit":"tokens","method":"sentence","sizeA":256,"overlapA":32,"sizeB":512,"overlapB":64,"sizeC":1024,"overlapC":128,"renderMode":"comparison","precision":1}'Send a POST request with your inputs as JSON. File parameters require a separate upload first.
POST https://api.elysiatools.com/en/api/tools/ai-rag-chunk-quality-scorer| Name | Type | Required | Description |
|---|---|---|---|
| document | textarea | Yes | — |
| unit | select | No | — |
| method | select | No | — |
| sizeA | number | Yes | — |
| overlapA | number | No | — |
| sizeB | number | Yes | — |
| overlapB | number | No | — |
| sizeC | number | Yes | — |
| overlapC | number | No | — |
| renderMode | select | No | — |
| precision | number | Yes | — |
HTML result
{
"result": "<div>Processed HTML content</div>",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Add this tool to your Model Context Protocol server so AI agents can list and call it.
Add this block to your MCP client configuration:
{
"mcpServers": {
"elysiatools-ai-rag-chunk-quality-scorer": {
"name": "ai-rag-chunk-quality-scorer",
"description": "Score candidate RAG chunk-splitting schemes for a document across four metrics — coherence (clean sentence/paragraph boundaries), coverage (topic focus / key-term concentration), context overlap health, and chunk-size consistency — then compare up to three schemes side by side and recommend the best. Pure offline heuristics, no model calls.",
"baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=ai-rag-chunk-quality-scorer",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}After connecting to the SSE endpoint, list the exposed tools:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Invoke the tool by its id, passing arguments built from its parameters:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "ai-rag-chunk-quality-scorer",
"arguments": {
"document": "Retrieval-augmented generation combines a retriever and a language model. The retriever finds relevant passages in a knowledge base. Chunking strategy strongly affects retrieval quality. Smaller chunks are precise but lose context. Larger chunks carry context but dilute relevance. Overlap between chunks preserves context across boundaries. Evaluate several schemes before indexing.",
"unit": "tokens",
"method": "sentence",
"sizeA": 256,
"overlapA": 32,
"sizeB": 512,
"overlapB": 64,
"sizeC": 1024,
"overlapC": 128,
"renderMode": "comparison",
"precision": 1
}
}
}Questions or issues? Contact [email protected]