A Model Context Protocol (MCP) server that enables AI assistants to securely search, analyze, and validate Splunk queries with built-in safety guardrails.
The Splunk MCP Server provides a standardized interface for AI assistants (like Claude, GitHub Copilot, etc.) to interact with Splunk Enterprise or Splunk Cloud. It implements the Model Context Protocol, allowing seamless integration between AI tools and your Splunk data.
- Smart Search Integration: Execute SPL queries with multiple output formats (JSON, Markdown, CSV, Summary)
- Built-in Safety Guardrails: Automatic validation to prevent destructive or resource-intensive queries
- Data Protection: Automatic sanitization of sensitive data (credit cards, SSNs)
- Dual Transport Support: Both SSE (Server-Sent Events) and stdio transports
- Rich Splunk Features: Access indexes, saved searches, and execute complex queries
- Docker Ready: Containerized deployment options for both implementations
The Model Context Protocol (MCP) is an open standard that enables seamless integration between AI assistants and external data sources. It provides:
- Standardized Communication: A common protocol for AI assistants to interact with external tools
- Security: Built-in authentication and authorization mechanisms
- Flexibility: Support for various transport mechanisms (stdio, SSE, WebSocket)
- Tool Discovery: Assistants can discover available tools and their capabilities
This project provides two feature-complete implementations:
- Built with FastMCP framework for simplified development
- Async/await architecture for efficient performance
- Includes comprehensive test suite and interactive tools
- Docker support with management scripts
- Full type safety with TypeScript
- Built on the official MCP SDK
- Compatible with Node.js 18+
- Production-ready with compiled JavaScript output
Choose your preferred implementation:
cd python
cp .env.example .env
# Edit .env with your Splunk credentials
pip install -e .
python server.py
cd typescript
cp .env.example .env
# Edit .env with your Splunk credentials
npm install
npm start
validate_spl
- Validate SPL queries for risks before executionsearch_oneshot
- Execute blocking searches with immediate resultssearch_export
- Stream large result sets efficientlyget_indexes
- List available Splunk indexes with metadataget_saved_searches
- Access saved search configurationsrun_saved_search
- Execute pre-configured saved searchesget_config
- Retrieve server configuration
The server includes intelligent guardrails to protect your Splunk environment:
- Risk Scoring: Queries are analyzed and assigned risk scores (0-100)
- Configurable Thresholds: Set your own risk tolerance levels
- Query Blocking: Dangerous queries are blocked before execution
- Performance Protection: Detects resource-intensive patterns
- Audit Trail: All queries are validated and logged
- Claude Desktop
- Claude Code
- VS Code Copilot
- Any MCP-compatible client
Both implementations follow the same architecture:
┌─────────────┐ MCP Protocol ┌─────────────┐ REST API ┌──────────┐
│ AI Assistant│ ◄─────────────────► │ MCP Server │ ◄─────────────► │ Splunk │
│ (Client) │ stdio/SSE/WS │ (This Repo) │ Port 8089 │ Instance │
└─────────────┘ └─────────────┘ └──────────┘
- Credentials: Store securely in
.env
files (never commit to version control) - Network: Use SSL/TLS for production deployments
- Permissions: Apply principle of least privilege for Splunk accounts
- Validation: All queries are validated before execution
- Sanitization: Sensitive data is automatically masked in outputs
splunk-mcp-server/
├── README.md # This file
├── LICENSE # MIT License
├── python/ # Python implementation
│ ├── README.md # Detailed Python documentation
│ ├── server.py # Main server implementation
│ ├── guardrails.py # Query validation logic
│ └── tests/ # Test suite and tools
└── typescript/ # TypeScript implementation
├── README.md # Detailed TypeScript documentation
├── server.ts # Main server implementation
├── guardrails.ts # Query validation logic
└── tests/ # Test scripts
We welcome contributions! Please see the implementation-specific README files for development setup and guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- MCP Documentation: modelcontextprotocol.io
- Splunk REST API: Splunk Documentation
Choose your preferred implementation above to get started with detailed setup instructions, configuration options, and usage examples.