2026-07-07 12:47 UTCIn-site rewrite4 min readUpdated: 2026-07-07 12:47 UTC

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This article introduces how Unstructured Platform handles over 60 unstructured data formats using a multi-layered parsing strategy, including rule-based parsers, OCR, and VLMs. It also discusses the trade-off in MCP server tool count and how to optimize context window usage.

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Unstructured Data Dominance

Most enterprise knowledge, aka the exact context your LLM-based application requires, lives in unstructured formats - PDFs, Word documents, PowerPoint presentations, internal wiki pages, emails, and chat messages. Traditional ETL tools weren't designed to handle these formats effectively. They are not built for unstructured data.

Handling a plethora of file formats

Each file type - whether it's a PDF, DOCX, PPT, or some other format - has its own internal structure and encoding. You need different parsers and extraction strategies for each format, significantly increasing complexity and maintenance overhead.

Unstructured Platform addresses this challenge by providing comprehensive data transformation capabilities that can handle over 60 different types of unstructured data formats. The platform employs a multi-layered approach: rule-based parsers efficiently handle structured documents like HTML, markdown, and Word files; custom fine-tuned OCR and document understanding models process image-based content from PDFs and PowerPoint presentations; and for the most challenging documents like noisy scans, handwritten notes, and complex tables and forms, the platform leverages state-of-the-art models such as, for example, Claude Sonnet and GPT-4o. The combination of parsing strategies ensures reliable content extraction across a wide range of document types while remaining cost-effective. Unstructured intelligently routes documents through the appropriate strategy, optimizing both processing speed and cost.

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Native File Access via Volumes Mount and stream files from Databricks Volumes without third-party connectors. Supports 60+ formats with OCR, VLM, and parsing capabilities baked in

GenAI-Optimized Data Transformation Mount and stream files from Databricks Volumes without third-party connectors. Supports 60+ formats with OCR, VLM, and parsing capabilities baked in

Delta Table Integration Mount and stream files from Databricks Volumes without third-party connectors. Supports 60+ formats with OCR, VLM, and parsing capabilities baked in

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Each connector requires credentials via .env, with full setup instructions available here.

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Unstructured API offers rich functionality with support for many different connectors and actions to manage them, as well as functionality to manage the workflows.

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The Tool Count Matters: Less Can Be More

Unstructured API offers rich functionality with support for many different connectors and actions to manage them, as well as functionality to manage the workflows. We quickly realized that matching API functionality to MCP tools one to one would not be the best MCP design. Too many tools available can overwhelm and confuse LLMs, making it harder for the model to find precise tools for the tasks at hand. More importantly, having an excessive number of tools creates a documentation challenge, as it quickly consumes the available context space that the LLM can use. This is why it's critical to find a balance in the number of tools available.

To ease the context window management challenge, we abstracted all of the connector management functionality. This resulted in a slashing of the context window usage by 5000 tokens!

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While the Unstructured MCP server supports many different tools, the reality is that not all tools are needed in every situation. Depending on your use case, you can restrict the MCP server to a smaller, more targeted set of tools. This will reduce the token usage for the documentation, may improve performance, and make it easier for LLMs to understand and execute their tasks.

/blog/leveraging-enterprise-specific-data-with-llms-how-unstructured-unlocked-100k-pages-of-irs-manuals

import os from dataclasses import dataclass from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerHTTP from pydantic_ai.tools import ToolDefinition

@dataclass class MCPServerHTTPWithSubsetTools(MCPServerHTTP): selected_tools: list[str] async def list_tools(self) -> list[ToolDefinition]: all_available_tools = await super().list_tools() filtered_tools = [ tool for tool in all_available_tools if tool.name in self.selected_tools ] return filtered_tools

s3_tools = MCPServerHTTPWithSubsetTools( url=os.getenv("UNSTRUCTURED_MCP_SERVER_URL"), selected_tools=[ "create_s3_source", "update_s3_source", "delete_s3_source", ], )

source_configuration_agent = Agent( system_prompt="Your goals is to configure S3 source in Unstructured platform", mcp_servers=[s3_tools], )

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