Unstructured API MCP Server

The Unstructured API MCP Server bridges the gap between AI assistants and the Unstructured platform’s powerful data‑processing capabilities. It exposes a rich set of tools that allow developers to programmatically manage sources, destinations, workflows, and jobs—all through the Model Context Protocol. By turning the Unstructured API into a first‑class MCP service, it lets Claude or other AI agents orchestrate complex data ingestion pipelines without leaving the conversational context.

Solving a Common Integration Pain Point

Many AI‑centric projects require automated extraction, transformation, and loading of unstructured data from diverse file types or web resources. Traditionally this involves writing custom adapters, handling authentication, and managing workflow orchestration manually. The Unstructured MCP server eliminates these hurdles by providing a unified toolset that abstracts the underlying HTTP endpoints. Developers can now issue high‑level commands such as or , and the server translates them into the appropriate Unstructured API calls, handling authentication tokens, error parsing, and response formatting automatically.

What the Server Does

• Source & Destination Management: List, create, update, or delete connectors that define where data comes from and where it goes. This includes file storage, cloud buckets, or database endpoints supported by Unstructured.
• Workflow Orchestration: Build end‑to‑end pipelines that tie a source to one or more destinations, specifying processing steps like OCR, table extraction, or language detection.
• Job Lifecycle Control: Trigger workflows, monitor job status, retrieve detailed results, and cancel running jobs—all via dedicated MCP tools.
• Intelligent Discovery: The tool aggregates completed jobs and surfaces source/destination metadata, enabling quick audit and reporting.

These capabilities are exposed as discrete tools that AI assistants can call with simple arguments, making it trivial to embed data‑processing logic into conversational flows.

Key Features Explained

• Declarative Workflow Creation: Define a workflow once and reuse it across projects. The tool accepts parameters like source ID, destination ID, and optional transformation steps, turning complex orchestration into a single command.
• Real‑time Job Monitoring: and provide instant visibility into the progress of each workflow run, allowing AI agents to inform users or trigger downstream actions based on completion status.
• Comprehensive Connector Coverage: The server currently supports all source and destination connectors listed in the Unstructured documentation, with plans to expand coverage as new integrations are released.
• Secure Credential Handling: All sensitive information is managed by the MCP server, so AI assistants never need to store or transmit secrets directly.

Real‑World Use Cases

1. Document Intake Automation: An AI assistant can ingest a batch of PDFs from an S3 bucket, run OCR and table extraction via a workflow, then ship the structured output to a database—all triggered by a user command.
2. Compliance Monitoring: Regularly schedule workflows that scan new files for policy violations, with the assistant alerting stakeholders when a job completes.
3. Data Lake Population: Automatically move newly uploaded data from a web crawler into a data lake, applying transformations on the fly without manual scripting.
4. Conversational Data Retrieval: A chatbot can query completed jobs to fetch extracted insights, presenting them in a natural language summary.

Integration into AI Workflows

Because the server adheres to MCP standards, any Claude‑compatible assistant can import its tools via a simple statement. Once imported, the assistant treats each tool like any other command: it can ask for confirmation, handle optional parameters, and chain results together. For example, after , the assistant can immediately call to confirm that the new source is available for workflow creation. This tight integration enables end‑to‑end automation where users interact with complex data pipelines through a single conversational interface.

Standout Advantages

• Zero‑Code Orchestration: Developers and non‑technical users alike can set up sophisticated data pipelines without writing code.
• Consistent API Surface: All Unstructured operations are wrapped in a single, well‑defined MCP schema, reducing cognitive load when switching between tools.
• Extensibility: New connectors or workflow steps can be added to the MCP server without changing client code, thanks to the declarative nature of tool definitions.
• Security by Design: Credentials and secrets remain on the server side, mitigating exposure risks in AI‑driven environments.

In summary, the Unstructured API MCP Server empowers AI assistants to manage end‑to‑end unstructured data workflows effortlessly, turning complex orches