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AI RAG Chunk Quality Scorer

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.

html· HTML resultOpen tool

Quick start

Call this tool from your code in three languages.

cURL
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}'

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API reference

Send a POST request with your inputs as JSON. File parameters require a separate upload first.

Endpoint

HTTP
POST https://api.elysiatools.com/en/api/tools/ai-rag-chunk-quality-scorer

Parameters

NameTypeRequiredDescription
documenttextareaYes—
unitselectNo—
methodselectNo—
sizeAnumberYes—
overlapAnumberNo—
sizeBnumberYes—
overlapBnumberNo—
sizeCnumberYes—
overlapCnumberNo—
renderModeselectNo—
precisionnumberYes—

Response format

HTML result

JSON
{
  "result": "<div>Processed HTML content</div>",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}

MCP integration

Add this tool to your Model Context Protocol server so AI agents can list and call it.

Server configuration

Add this block to your MCP client configuration:

mcp.json
{
  "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"
    }
  }
}

List available tools

After connecting to the SSE endpoint, list the exposed tools:

tools/list
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

Call this tool

Invoke the tool by its id, passing arguments built from its parameters:

tools/call
{
  "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
}
  }
}
Chain multiple tools in one session with a comma-separated toolId list, e.g. /mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp (max 20).

Questions or issues? Contact [email protected]

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