CerevoxAI/cerevox-mcp-server
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Cerevox MCP Server is a Model Context Protocol server that provides AI agents with access to the Cerevox API suite for document parsing, semantic search, and account management.
Cerevox MCP Server
Model Context Protocol (MCP) server for Cerevox AI - The Data Layer for AI Agents.
This MCP server exposes the full Cerevox API suite through the Model Context Protocol, enabling AI agents to:
- Parse documents with industry-leading accuracy (Lexa API)
- Search and query document collections with RAG (Hippo API)
- Manage accounts and users (Account API)
Features
Lexa - Document Parsing
- Parse documents from URLs with AI-powered extraction
- Support for PDF, DOCX, TXT, HTML, and 12+ formats
- Extract text, tables, images, and metadata
- Monitor processing jobs in real-time
Hippo - RAG & Semantic Search
- Create and manage document folders
- Upload files from URLs for processing
- Create chat sessions for Q&A
- Ask questions with AI-powered answers and source citations
- Retrieve conversation history
- Manage files and folders
Account - User Management
- Get account information and usage metrics
- View plan details and limits
- List and manage users
- Track API usage and billing
Installation
Prerequisites
- Python 3.9 or higher
- Cerevox API key (get one here)
Install from source
# Clone the repository
git clone https://github.com/CerevoxAI/cerevox-mcp-server.git
cd cerevox-mcp-server
# Install in development mode
pip install -e .
Install from PyPI (coming soon)
pip install cerevox-mcp-server
Configuration
Set up your API key
The server requires a Cerevox API key. Set it as an environment variable:
export CEREVOX_API_KEY="your-api-key-here"
Or add it to your shell configuration file (~/.bashrc, ~/.zshrc, etc.):
echo 'export CEREVOX_API_KEY="your-api-key-here"' >> ~/.zshrc
source ~/.zshrc
Configure with Claude Desktop
Add this to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"cerevox": {
"command": "python",
"args": ["-m", "cerevox_mcp_server"],
"env": {
"CEREVOX_API_KEY": "your-api-key-here"
}
}
}
}
Configure with other MCP clients
For other MCP clients, refer to their documentation for connecting to MCP servers. Generally, you'll need to:
- Point the client to the server:
python -m cerevox_mcp_server - Ensure the
CEREVOX_API_KEYenvironment variable is set
Usage Examples
Document Parsing with Lexa
Parse a document and extract structured content:
Use the lexa_parse_document tool to parse this PDF: https://example.com/document.pdf
The AI will extract text, tables, and metadata from the document.
RAG Search with Hippo
Create a folder, upload documents, and ask questions:
1. Create a folder called "research_papers" with ID "research"
2. Upload this file: https://arxiv.org/pdf/2301.00001.pdf
3. Create a chat session for the "research" folder
4. Ask: "What are the main findings of this paper?"
The AI will:
- Create the folder
- Upload and process the document
- Create a chat session
- Answer your question using RAG with source citations
Account Management
Check your account usage:
1. Get my account information
2. Show my usage metrics
3. List all users in the account
Available Tools
Lexa Tools
| Tool | Description |
|---|---|
lexa_parse_document | Parse document from URL with AI extraction |
lexa_get_job_status | Check status of parsing job |
Hippo Folder Tools
| Tool | Description |
|---|---|
hippo_create_folder | Create a new document folder |
hippo_list_folders | List all folders |
hippo_get_folder | Get folder details |
hippo_delete_folder | Delete a folder and all contents |
Hippo File Tools
| Tool | Description |
|---|---|
hippo_upload_file_url | Upload file from URL |
hippo_list_files | List files in a folder |
hippo_get_file | Get file details |
hippo_delete_file | Delete a file |
Hippo Chat/Q&A Tools
| Tool | Description |
|---|---|
hippo_create_chat | Create chat session for Q&A |
hippo_list_chats | List all chat sessions |
hippo_ask_question | Ask question with RAG (primary tool) |
hippo_get_chat_history | Get conversation history |
hippo_get_question_details | Get full details of a Q&A |
hippo_delete_chat | Delete chat session |
Account Tools
| Tool | Description |
|---|---|
account_get_info | Get account information |
account_get_usage | Get usage metrics |
account_get_plan | Get plan details and limits |
account_list_users | List all users |
account_get_current_user | Get current user info |
Development
Setup development environment
# Clone and install with dev dependencies
git clone https://github.com/CerevoxAI/cerevox-mcp-server.git
cd cerevox-mcp-server
pip install -e ".[dev]"
Run tests
pytest
Code formatting
black src/
Type checking
mypy src/
Architecture
The server is built on:
- MCP Python SDK - Model Context Protocol implementation
- cerevox-python - Official Cerevox Python SDK
- AsyncIO - Asynchronous operations for optimal performance
Tool Design
Each tool follows a consistent pattern:
- Input validation - Validates required parameters
- Client initialization - Reuses authenticated clients
- API call - Executes the Cerevox API operation
- Response formatting - Returns structured JSON responses
- Error handling - Provides clear error messages
Authentication
The server handles authentication automatically:
- API key loaded from
CEREVOX_API_KEYenvironment variable - Clients initialized lazily on first use
- Sessions maintained for optimal performance
- Automatic token refresh handled by cerevox-python SDK
Troubleshooting
"CEREVOX_API_KEY environment variable not set"
Make sure you've set the environment variable:
export CEREVOX_API_KEY="your-api-key-here"
"Connection refused" or "Server not responding"
Ensure the MCP server is running and your client is configured correctly. Check logs for detailed error messages.
"Authentication failed"
Verify your API key is valid and has the necessary permissions. Get a new key at https://cerevox.ai
Document parsing is slow
Large documents may take several minutes to process. Use the lexa_get_job_status tool to monitor progress.
Examples
Complete RAG Workflow
# This would be done through an MCP client like Claude Desktop
# 1. Create a folder for your documents
"Create a Hippo folder with ID 'my_docs' and name 'My Documents'"
# 2. Upload documents
"Upload https://example.com/report.pdf to the 'my_docs' folder"
# 3. Wait for processing (check file status)
"List files in the 'my_docs' folder to check processing status"
# 4. Create a chat session
"Create a chat session for the 'my_docs' folder"
# 5. Ask questions
"Ask in chat [chat_id]: What are the key recommendations in the report?"
# 6. Follow-up questions
"Ask in chat [chat_id]: Can you elaborate on the financial projections?"
# 7. Get conversation history
"Show me the conversation history for chat [chat_id]"
Document Analysis
# Parse a document and analyze its content
"Parse this document: https://example.com/contract.pdf using advanced mode"
# The response will include:
# - Extracted text content
# - Number of pages
# - Number of tables found
# - Content preview
Account Monitoring
# Check account status and usage
"Get my account information"
"Show my usage metrics"
"What's my current plan and its limits?"
Support
- Documentation: https://docs.cerevox.ai
- GitHub Issues: https://github.com/CerevoxAI/cerevox-mcp-server/issues
- Discord: https://discord.gg/cerevox
- Email: support@cerevox.ai
Contributing
We welcome contributions! Please see our for details.
License
This project is licensed under the MIT License - see the file for details.
Links
Made with ❤️ by the Cerevox team
Happy Building! 🔍 🦛 ✨