Rossum MCP & Agent

MCP server and AI agent toolkit for intelligent document processing with Rossum.

AI-powered Rossum orchestration: Document workflows conversationally, debug pipelines automatically, and configure automation through natural language.

Conversational AI toolkit for the Rossum intelligent document processing platform. Transforms complex workflow setup, debugging, and configuration into natural language conversations through a Model Context Protocol (MCP) server and specialized AI agent.

[!IMPORTANT] This project has moved to a company-private GitLab for a major overhaul. This public repository is temporarily archived and will not receive updates during that period.

[!NOTE] This is not an official Rossum project. It is a community-developed integration built on top of the Rossum API, not a product (yet).

Set up a complete customer organization with queues, schemas, validations, duplicate detection, email notifications, and UI configuration:

1. Create two new queues: Invoices and Credit Notes. 2. Update schemas w.r.t. schema specification (Invoices with 15 fields including line items table, Credit Notes as-is) 3. Add a computed field "The Net Terms" to Invoices queue (Due Date - Issue Date → Net 15/30/Outstanding) 4. Implement duplicate document detection on Document ID 5. Add business validations: total amount cap, line items sum check, quantity × unit price check 6. Add email notification extension on document status change to 'to_review' 7. Update Invoice queue UI settings to display 8 key fields 8. Verify setup by uploading a sample invoice twice (testing duplicate detection)

- Queue & Schema Setup: Creates queues with detailed field specifications including line items tables
- Computed Fields: Adds derived fields with business logic (date difference categorization)
- Duplicate Detection: Configures document-level deduplication with user-facing messages
- Business Validations: Implements multi-rule validation (amount caps, sum checks, arithmetic checks)
- Email Notifications: Sets up templated email alerts triggered by document state changes
- UI Configuration: Customizes queue column display for operational efficiency
- End-to-End Verification: Validates the entire setup with real document uploads

This example showcases the agent's ability to set up a production-ready organization from scratch - all from a single conversational prompt.

Automatically analyze and document all hooks/extensions configured on a queue:

Briefly explain the functionality of every hook based on description and/or code one by one for a queue 2042843. Store output in extension_explanation.md

- Lists all hooks/extensions on the specified queue
- Analyzes each hook's description and code
- Generates clear, concise explanations of functionality
- Documents trigger events and settings
- Saves comprehensive documentation to a markdown file

This example shows how the agent can analyze existing automation to help teams understand their configured workflows.

Set up a complete document splitting and sorting pipeline with training queues, splitter engine, automated hooks, and intelligent routing:

1. Create three new queues in workspace 1777693 - Air Waybills, Certificates of Origin, Invoices. 2. Set up the schema with a single enum field on each queue with a name Document type (document_type). 3. Upload documents from folders air_waybill, certificate_of_origin, invoice in examples/data/splitting_and_sorting/knowledge to corresponding queues. 4. Annotate all uploaded documents with a correct Document type, and confirm the annotation. - Beware document types are air_waybill, invoice and certificate_of_origin (lower-case, underscores). - IMPORTANT: After confirming all annotations, double check, that all are confirmed/exported, and fix those that are not. 5. Create three new queues in workspace 1777693 - Air Waybills Test, Certificates of Origin Test, Invoices Test. 6. Set up the schema with a single enum field on each queue with a name Document type (document_type). 7. Create a new engine in organization 1, with type = 'splitter'. 8. Configure engine training queues to be - Air Waybills, Certificates of Origin, Invoices. - DO NOT copy knowledge. - Update Engine object. 9. Create a new schema that will be the same as the schema from the queue 3885208. 10. Create a new queue (with splitting UI feature flag!) with the created engine and schema in the same workspace called: Inbox. 11. Create a python function-based the Splitting & Sorting hook on the new inbox queue with this settings: Functionality: Automatically splits multi-document uploads into separate annotations and routes them to appropriate queues. Split documents should be routed to the following queues: Air Waybills Test, Certificates of Origin Test, Invoices Test Trigger Events: - annotation_content.initialize (suggests split to user) - annotation_content.confirm (performs actual split) - annotation_content.export (performs actual split) How it works: Python code Settings: - sorting_queues: Maps document types to target queue IDs for routing - max_blank_page_words: Threshold for blank page detection (pages with fewer words are considered blank) 12. Upload 10 documents from examples/data/splitting_and_sorting/testing folder to inbox queues.

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Queue Orchestration: Creates 7 queues (3 training + 3 test + 1 inbox) with consistent schemas

Knowledge Warmup: Uploads and annotates 90 training documents to teach the engine

Splitter Engine: Configures an AI engine to detect document boundaries and types

Hook Automation: Sets up a sophisticated webhook that automatically:

- Splits multi-document PDFs into individual annotations
- Removes blank pages intelligently
- Routes split documents to correct queues by type
- Suggests splits on initialization and executes on confirmation

End-to-End Testing: Validates the entire pipeline with test documents

This example showcases the agent's ability to orchestrate complex workflows involving multiple queues, engines, schemas, automated hooks with custom logic, and intelligent document routing - all from a single conversational prompt.

- rossum-mcp/- MCP server exposing the Rossum API as a fully-typed tool surface for AI assistants (seeavailable tools)
- rossum-agent/- Specialized AI agent with skills, sub-agents, and a REST API (seeavailable toolsandskills & sub-agents)

- rossum-agent-client/- Typed Python client for the Rossum Agent API
- rossum-agent-client-ts/- Typed TypeScript client for the Rossum Agent API
- rossum-agent-tui/- Terminal UI for development and testing (Node.js)

Prerequisites: Python 3.12+,uv,Rossum accountwith[API credentials

git clone https://github.com/rossumai/rossum-agents.git cd rossum-agents # Install all packages with all features uv sync --all-extras # AWS Bedrock (the agent uses Claude via Bedrock) export AWS_PROFILE="rossum-dev" export AWS_REGION="eu-west-1" # Start PostgreSQL (session storage) and Valkey (change tracking) docker-compose up -d postgres valkey # Run the agent REST API uv run rossum-agent-api

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