MCP For Claude Marketplace
RAG Payload Metadata Extractor

RAG Payload Metadata Extractor MCP Connector for Claude

A+

Extracts metadata and calculates data density for RAG source documents.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides deterministic tools to validate the structural integrity of RAG source documents. It uses strict pattern matching to identify metadata like Title, URL, Date, and Author. Use extract_document_metadata to pull specific fields, calculate_size_metrics to compare metadata byte size against core content, and audit_document_integrity to determine if a document is fit for RAG ingestion based on its header validity and success rate.

ragmetadataextractiondocument-integritydata-quality

3 tools expose this connector's capabilities to your AI agent.

calculate_size_metrics

Supply both the document content and metadata JSON. Quantify the data distribution between metadata and the core body of a document

extract_document_metadata

Provide the full document content as the primary input. Extract specific metadata fields from a raw document string

audit_document_integrity

Pass the full document content for evaluation. Provide a high-level summary of whether a document is fit for RAG ingestion

See how to talk to your AI agent using RAG Payload Metadata Extractor.

Can you extract the metadata from this document: 'Title: Report A, URL: https://a.com, Date: 2023, Author: John'?

The extracted metadata includes Title: Report A, URL: https://a.com, Date: 2023, and Author: John.

Is this document suitable for RAG ingestion?

The document is fit for RAG ingestion as it contains a valid header and all required metadata fields were successfully identified.

What is the metadata to content ratio for this file?

The metadata occupies 120 bytes, while the core content occupies 1500 bytes, resulting in a ratio of 0.08.

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.

Related Connectors

XML to Dict Parser

XML to Dict Parser MCP

3 tools Official

Converts XML strings into deterministic dictionary structures for stable LLM outputs.

A+ View details →
Alphamoon

Alphamoon MCP

12 tools Official

Extract data from documents using AI-powered OCR and intelligent document processing for contracts, invoices, and forms.

A+ View details →
Docparser

Docparser MCP

10 tools Official

Equip your AI agent to extract data from documents, manage parsers, and track extraction results via the Docparser API.

A+ View details →
Extracta

Extracta MCP

10 tools Official

Automate data extraction via Extracta — process documents into structured JSON, handle AI classification, and audit extraction history directly from any AI agent.

A+ View details →