MCP ServerSTDIOOfficialv0.1.1

Intelagentstudios MCP File Processor MCP Server

Extract text, keywords, and language metadata from files, then chunk content for RAG pipelines. Built for developers integrating document processing into AI workflows.

io.github.IntelagentStudios/mcp-file-processor

Hosted URL

Local install

Transport

STDIO

Auth

No auth required

What the Intelagentstudios MCP File Processor MCP server does

How models use it and what it is built for.

Extract text, keywords, and language metadata from files, then chunk content for RAG pipelines. Built for developers integrating document processing into AI workflows.

Connect to Intelagentstudios MCP File Processor

Local install — runs as a subprocess.

npx @intelagent/mcp-file-processor@0.1.1

Resources

Where to find authoritative docs and source for Intelagentstudios MCP File Processor.

Example prompts for Intelagentstudios MCP File Processor

Paste any of these into Agent Studio after connecting Intelagentstudios MCP File Processor.

  • Extract text and detect language from this PDF file
  • Split this document into chunks optimized for RAG embedding
  • Pull keywords from a text file for search indexing
  • What languages does this server support for detection?

Intelagentstudios MCP File Processor MCP server — FAQ

Common questions about connecting and running Intelagentstudios MCP File Processor.

  • What file formats does this MCP server support?

    The registry lists text extraction and keyword extraction as core capabilities, but the specific supported formats (PDF, DOCX, TXT, etc.) are not detailed in available documentation. Check the project repository or run the server with a test file to confirm format support.

  • How do I install and run the mcp-file-processor server?

    Install via npm with `npx @intelagent/mcp-file-processor@0.1.1`. The server uses stdio transport, so it communicates with your MCP client through standard input/output streams. Refer to the MCP client documentation for connection setup.

  • Can I use this server to prepare documents for vector databases?

    Yes—the chunking capability is specifically designed for RAG (Retrieval-Augmented Generation) pipelines, making it suitable for preparing documents before embedding and storing in vector databases.

  • What languages does the language detection support?

    Language detection is listed as a core feature, but the specific languages supported are not documented in the registry metadata. Consult the project repository or test with your target languages.

  • Is there a cost or authentication required to use this server?

    This is an open-source npm package with no apparent API keys or external service dependencies mentioned in the registry. Installation and usage are free, though you should verify licensing terms in the project repository.

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