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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use RAG Metadata Extractor with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Extracts metadata and calculates data density for RAG source documents.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 3 capabilities

The complete RAG Metadata Extractor capability set.

These are the exact actions your AI can choose when you ask it to work with RAG Metadata Extractor.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through RAG Metadata Extractor.

  1. 01

    Calculate size metrics

    Quantify the data distribution between metadata and the core body of a document

  2. 02

    Audit document integrity

    Provide a high-level summary of whether a document is fit for RAG ingestion

  3. 03

    Extract document metadata

    Extract specific metadata fields from a raw document string

Observed, not estimated

830ms average. Fast in production.

RAG Metadata Extractor is checked daily against the live service.

Daily averagePeak 1002ms
Aug 20Today
Fastest day
651ms
Slowest day
1002ms
14-day trend
Improving-8%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of RAG Metadata Extractor, so you can see the experience inside your AI.

It does not authenticate your account with RAG Metadata Extractor. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

RAG Metadata Extractor Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_xxeAOzNGYrtxsJJQwiQoGCgrfEDul7KeBkVQnRCA/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — RAG Metadata Extractor capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "rag-payload-metadata-extractor-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_xxeAOzNGYrtxsJJQwiQoGCgrfEDul7KeBkVQnRCA/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions RAG Metadata Extractor owners ask.

  • 01

    How does the extraction process work?

    The server uses deterministic regex patterns to identify metadata keys within a document's header. You can use extract_document_metadata to retrieve these fields.

  • 02

    What defines a document as fit for RAG?

    A document is considered fit for RAG if it has a valid metadata header and a 100% success rate in field extraction, as verified by audit_document_integrity.

  • 03

    Can I measure the density of my document metadata?

    Yes, the calculate_size_metrics capability provides the byte-size ratio between the extracted metadata and the core document content.

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