MCP Server - Knowledge Base Tools
MCP (Model Context Protocol) server exposing knowledge base operations as tools for AI agents.
Overview
This MCP server provides tools for interacting with the Django backend API to manage and query the knowledge base. All tools require JWT authentication and automatically handle tenant-aware routing.
Features
- JWT Authentication: Token verification using shared secret with Django backend
- Tenant-aware: Automatically extracts tenant from JWT and routes to correct backend instance
- 8 Knowledge Base Tools: Complete set of operations for navigating and managing the knowledge graph
Installation
Create and activate virtual environment (recommended):
python3 -m venv venv # On macOS/Linux: source venv/bin/activate # On Windows: # venv\Scripts\activateInstall dependencies:
pip install -r requirements.txtConfigure environment variables:
cp .env.example .env # Edit .env with your configuration
Configuration
Environment Variables
MCP_JWT_SECRET_KEY: Secret key for JWT token verification (can useCHAINLIT_JWT_SECRET_KEYas fallback)MCP_BACKEND_URL: Backend URL with tenant placeholder, e.g.,http://tenant.localhost:8000(can useBACKEND_URLas fallback)
Example .env file:
MCP_JWT_SECRET_KEY=your-secret-key-here
MCP_BACKEND_URL=http://tenant.localhost:8000
Available Tools
1. get_root_themes_tool
Get all root theme nodes (top-level folders without parents). Entry point for navigating the knowledge base.
2. get_folder_tree_tool
Get complete folder tree under a theme node. Returns only themes recursively, excluding skills and knowledge nodes.
3. semantic_search_tool
Perform semantic search across themes, skills, and knowledge nodes using vector similarity.
4. get_node_children_tool
Get direct children of a node for downward navigation in the hierarchy.
5. get_node_parents_tool
Get direct parent nodes for upward navigation in the hierarchy.
6. create_folder_tool
Create a new theme (folder) node at root or under a parent theme.
7. create_skill_tool
Create a new skill node under a parent theme.
8. generate_learning_tools_tool
Generate learning tools (knowledge nodes with questions) for a skill using AI.
Running the Server
Make sure your virtual environment is activated before running the server.
Start the HTTP/SSE server:
# Activate venv first
source venv/bin/activate # On macOS/Linux
# venv\Scripts\activate # On Windows
python server.py
The server will start on http://0.0.0.0:8100 by default (configurable via environment variables).
Configuration options:
MCP_HOST: Host to bind (default:0.0.0.0)MCP_PORT: Port number (default:8100- port 8000 is used by the Django backend)MCP_TRANSPORT: Transport type -sse(Server-Sent Events) orstreamable-http(default:sse)
With MCP Inspector (for testing):
Start the server:
python server.pyIn MCP Inspector, configure the connection:
- Type:
sse - URL:
http://localhost:8100/sse(uselocalhostnot0.0.0.0in browser)
Example configuration:
{ "mcpServers": { "knowledge-base": { "type": "sse", "url": "http://localhost:8100/sse" } } }Note: The server binds to
0.0.0.0but you must uselocalhostor127.0.0.1in browser-based clients.- Type:
Usage
All tools require a jwt_token parameter containing the user's JWT authentication token. The token should:
- Be a valid JWT signed with the same secret as configured in
MCP_JWT_SECRET_KEY - Contain a
tenantclaim for tenant-aware routing - Be passed as a Bearer token or raw token string
Example tool call:
result = get_root_themes_tool(
jwt_token="eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
)
Architecture
auth.py: JWT token verification and tenant extractiontools.py: Core tool functions that interact with Django backendserver.py: FastMCP server setup and tool registration
Security
- All tools verify JWT tokens before processing requests
- Tenant is extracted from token payload (never from user input)
- Backend URL is constructed from token, ensuring tenant isolation
- Invalid or expired tokens return error responses
Error Handling
All tools return a dictionary with:
success: trueand data on successsuccess: falseanderror: "message"on failure
Development
The server uses FastMCP, which automatically handles:
- Tool registration
- Request/response serialization
- Error handling
- Logging
Related Files
shad/agents/tools.py: Original LangChain tools (reference implementation)backend/learn/: Django backend API endpoints