Cerevox MCP Server
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Cerevox MCP ServerParse this PDF from URL: https://example.com/report.pdf"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: Rememberizer MCP Server
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-serverConfiguration
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 ~/.zshrcConfigure 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_serverEnsure 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.pdfThe 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 accountAvailable Tools
Lexa Tools
Tool | Description |
| Parse document from URL with AI extraction |
| Check status of parsing job |
Hippo Folder Tools
Tool | Description |
| Create a new document folder |
| List all folders |
| Get folder details |
| Delete a folder and all contents |
Hippo File Tools
Tool | Description |
| Upload file from URL |
| List files in a folder |
| Get file details |
| Delete a file |
Hippo Chat/Q&A Tools
Tool | Description |
| Create chat session for Q&A |
| List all chat sessions |
| Ask question with RAG (primary tool) |
| Get conversation history |
| Get full details of a Q&A |
| Delete chat session |
Account Tools
Tool | Description |
| Get account information |
| Get usage metrics |
| Get plan details and limits |
| List all users |
| 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
pytestCode 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 variableClients 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 previewAccount 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 Contributing Guide for details.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Links
Made with ❤️ by the Cerevox team
Happy Building! 🔍 🦛 ✨
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