Thanks to the rapid advances in LLMs, software is evolving from rigid, pre-programmed steps into agentic workflows. Instead of running pre-programmed steps, AI agents can plan their own sequence of actions, retrieve the right data, and take actions dynamically, all without human intervention.

At fileAI, we saw an opportunity. Our platform already processed millions of files for enterprises like Toshiba, MSIG, UOB, and KFC, turning unstructured content into structured, verified data. But as agents became more capable, we needed to become the go-to file analytics and automation layer for enterprises building agentic capabilities and the new generation of AI-native companies.

To make our document processing platform accessible to AI agents and agentic workflows across different environments and applications, we needed to build a production-grade Model Context Protocol (MCP) server. We partnered with Tadata to bring it to life.

The Challenge

Multinational enterprises across document-heavy sectors like insurance, finance, and supply chain are building agentic systems that need OCR capabilities as part of larger workflows. These AI agents need to seamlessly integrate multiple tools and services - document processing, data extraction, database queries, and more - without requiring developers to manually orchestrate each step.

MCP enables this by allowing AI agents to directly access fileAI's document processing capabilities as native tools within their environment. Instead of developers writing custom integration code for each service, AI agents can discover and use our OCR capabilities alongside other tools to build complex automated workflows.

We needed an MCP server that would make fileAI a natural component in these multi-tool agentic systems without requiring engineering overhead to build and maintain the integration infrastructure.

Our Implementation Process

Step 1: API Preparation Our existing API was already well-documented with comprehensive OpenAPI specifications, making it ideal for MCP integration. 

Step 2: Platform Integration We connected our OpenAPI specification to Tadata's platform, which automatically analyzed our endpoint structure and generated MCP tool definitions.

Step 3: Authentication Setup We integrated Tadata with our existing API key authentication system. The MCP server handles authentication transparently, so AI agents can access fileAI capabilities without exposing credentials.

Step 4: Tool Configuration We selected which endpoints to expose as MCP tools, focusing on core document processing, schema management, and file handling capabilities. Tool names and descriptions were improved to be more LLM-friendly to improve user experience.

Step 5: Testing and Launch We validated the MCP server across multiple AI environments to ensure all endpoints and workflows functioned correctly, with particular focus on the enterprise agentic use cases our customers were building—eKYC systems, automated compliance reporting, and real-time data validation workflows.

What This Enables

With our MCP server, both AI agents and developers can now natively use fileAI's document processing capabilities as part of their workflows:

• Process documents through fileAI's OCR directly within AI development environments
• Access our full range of our models for different languages and document types
• Query and retrieve processed documents seamlessly
• Chain fileAI's capabilities with other tools in complex agentic workflows

Rather than developers having to write integration code, AI agents can discover and use fileAI's functionality automatically. This means an AI agent can process a document, extract structured data, and immediately use that information with other tools - all without manual intervention.

For agentic systems, this makes fileAI a natural component in automated workflows where document processing is just one step in a larger sequence of AI-powered tasks.

Impact

Our MCP server now serves as infrastructure for our developer empowerment strategy while maintaining our enterprise sales capabilities. The MCP server delivered value in two distinct ways: enabling external developers to integrate fileAI's document AI directly into their agentic workflows, while also allowing our sales team to create tailored, interactive demos for prospects entirely through chat.

The future of API adoption increasingly flows through AI development environments. Making our document processing platform natively accessible in these environments positions fileAI for sustained growth in the AI-first development landscape.

Try fileAI's MCP Server at mcp.file.ai 

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How we built our MCP server to power agentic workflows

The rise of AI, large language models, and agents are reshaping how software works. Instead of following rigid, pre-programmed steps, AI agents can now plan actions, retrieve the right data, and adapt dynamically without constant human input.

At fileAI, we saw an opportunity to extend this shift. Our platform already processes millions of files for enterprises such as Toshiba, MSIG, UOB, and KFC, turning unstructured content into structured, verified data. As agentic systems advanced, we recognized the need for fileAI to be the file analytics and automation layer for enterprises and AI-native companies alike.

To make our platform accessible to AI agents across environments and applications, we needed a production-grade Model Context Protocol (MCP) server. We partnered with Tadata to make it real.

The challenge

Enterprises in document-heavy sectors such as insurance, finance, and supply chain are building agentic systems that depend on OCR as part of larger workflows. These systems need more than just document processing—they require seamless integration with data extraction, databases, and other services, all without developers manually wiring everything together.

MCP enables this by allowing AI agents to access fileAI’s document processing directly as native tools. Instead of writing custom integration code, developers and AI agents can discover and use our capabilities automatically, making fileAI a natural component of multi-tool workflows.

The goal was clear: build an MCP server that would integrate fileAI into agentic systems without adding engineering overhead.

How we built it

• Step 1: API Preparation Our existing API was already well-documented with comprehensive OpenAPI specifications, making it ideal for MCP integration. 
• Step 2: Platform Integration We connected our OpenAPI specification to Tadata's platform, which automatically analyzed our endpoint structure and generated MCP tool definitions.
• Step 3: Authentication Setup We integrated Tadata with our existing API key authentication system. The MCP server handles authentication transparently, so AI agents can access fileAI capabilities without exposing credentials.
• Step 4: Tool Configuration We selected which endpoints to expose as MCP tools, focusing on core document processing, schema management, and file handling capabilities. Tool names and descriptions were improved to be more LLM-friendly to improve user experience.
• Step 5: Testing and Launch We validated the MCP server across multiple AI environments to ensure all endpoints and workflows functioned correctly, with particular focus on the enterprise agentic use cases our customers were building—eKYC systems, automated compliance reporting, and real-time data validation workflows.

What this enables

With our MCP server, AI agents and developers can:

• Process documents through fileAI’s OCR directly within development environments
  
  
• Access our models across multiple languages and document types
  
  
• Retrieve processed documents seamlessly
  
  
• Chain fileAI’s tools with other systems in complex workflows
  
  

Instead of writing custom integrations, AI agents can process a document, extract structured data, and immediately act on it with other tools—automatically. For agentic systems, fileAI becomes a building block in larger AI-powered processes.

The impact

Our MCP server strengthens two areas of our strategy. Developers can now integrate fileAI directly into their agentic workflows, while our sales teams can create tailored, interactive demos entirely through chat.

As AI-first development accelerates, APIs will increasingly be consumed inside agentic environments. By making our document processing platform natively accessible there, fileAI is positioned to serve both today’s enterprises and tomorrow’s AI-native builders.

👉 Try fileAI’s MCP server at mcp.file.ai

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