Splunk MCP Server | MCPBro - MCP Market

What is Splunk MCP Server?

Splunk MCP Server is a powerful connector between Splunk and the Model Context Protocol, enabling large language models to communicate with Splunk services in a standardized, agent-friendly way. It abstracts Splunk's complex API and management operations as discoverable and callable "tools," automating tasks like search, user management, index inspection, and key-value store access, all via natural language or programmatic requests.

How to Configure

Set Environment Variables
Configure connection settings for Splunk and MCP by exporting environment variables such as SPLUNK_HOST, SPLUNK_PORT, SPLUNK_USERNAME, and SPLUNK_PASSWORD (or SPLUNK_TOKEN). Adjust VERIFY_SSL as needed for certificate validation.
Choose Operation Mode
Decide on a mode: SSE (default, for live agent connections), API (REST endpoints), or STDIO (for direct integration with local AI agents like Claude Desktop).
Install Dependencies
Use uv, poetry, or pip to install all required Python packages.
Start the Application
Depending on your setup, run the server in your chosen mode using Python or Docker commands.
- For SSE mode: python splunk_mcp.py
- For API mode: python splunk_mcp.py api
- For STDIO mode: python splunk_mcp.py stdio
(Optional) Run in Docker
Use docker compose to build and deploy the service in any supported mode.

How to Use

For LLMs/AI Agents:
Connect your AI assistant client (such as Claude Desktop or a web-based agent) to the Splunk MCP Server's endpoint (SSE, API, or STDIO). The client can then discover and invoke tools (Splunk operations) as needed.
Interact via Tools:
Use natural language or programmatic requests to trigger Splunk searches, view indexes, manage users, or operate on KV stores. The MCP layer translates these into secure, permissioned queries to your Splunk instance.
Testing & Validation:
Run built-in health checks and integration tests to ensure connectivity and functionality. Test searches and tool calls are possible from both CLI and Docker containers.
API & SSE Endpoints: Access RESTful or SSE endpoints for integrating Splunk insights into third-party dashboards or automations.

Key Features

Multiple Operation Modes: SSE, API, and STDIO to suit a wide range of clients and agent interfaces.
Natural Language Splunk Search: Converts plain language queries into Splunk search jobs, with flexible parameters.
Index & User Management: Enumerate and inspect indexes/users directly from the agent interface.
KV Store Operations: Full CRUD on Splunk KV store collections.
Async Performance: Uses asynchronous programming for high throughput and responsive operations.
Comprehensive Logging: Detailed, emoji-augmented logs increase observability and ease troubleshooting.
Customizable SSL Security: Choose between strict certificate verification and relaxed modes for development.
Deep Error Handling: Clear, structured error messages for all tool invocations.
Health & Connectivity Checks: Built-in tool and endpoint to verify service readiness and Splunk connectivity.

Use Cases

AI-Powered IT Operations: Automate routine Splunk queries, index management, and user lookups from an LLM-based assistant.
Security Analysis: Use LLMs to search logs, analyze threat patterns, and fetch contextual data for investigation flows.
DevOps Monitoring: Enable chatops or agent workflows to query system indexes, check health, and manage log storage.
Automated Reporting: Generate reports or extract data from Splunk with natural language commands, reducing manual dashboard use.
Custom AI Agents for Splunk: Build LLM-powered bots or copilots that interact programmatically with Splunk via MCP.

FAQ

Q1: What operating modes does Splunk MCP Server support, and which should I use?
A: It supports SSE (for real-time AI/web clients), API (REST calls for programmatic integrations), and STDIO (for direct agent connections like with Claude Desktop). SSE is the default; choose based on your integration scenario.

Q2: How do I secure my Splunk credentials and ensure a safe deployment?
A: Always use environment variables for credentials and never commit them to source control. For production, enforce VERIFY_SSL=true, use Docker secrets where possible, and restrict port exposure.

Q3: Can I use Splunk MCP Server with Splunk Cloud and Enterprise editions?
A: Yes, it supports both Splunk Enterprise (on-prem) and Splunk Cloud. Configuration is identical; just ensure the endpoint is accessible and properly authenticated.

Q4: How can I troubleshoot failed Splunk searches or connection issues?
A: Examine the MCP server logs (with detailed emoji/logging), review error messages in tool responses, check environment variable correctness, and verify Splunk API/network connectivity.

Q5: Can I add custom tools or extend the MCP server for other Splunk operations?
A: Absolutely—Splunk MCP is extensible. You can implement new MCP tools as Python async functions and register them to provide additional Splunk or third-party integrations.

Tool Name	Description
list_tools	Returns a list of all MCP tools available on the server, including descriptions and supported parameters, allowing agents to discover available operations.
health_check	Checks connectivity to Splunk and returns a list of detected Splunk apps; used to verify successful setup and connectivity.
ping	Simple endpoint to verify the MCP server is alive and responding; returns 'pong' with server info.
current_user	Returns the identity and permission details of the currently authenticated Splunk user.
list_users	Retrieves a list of all Splunk users, including their roles and status, for administrative inspection.
list_indexes	Lists all Splunk indexes accessible with current credentials, including visibility into custom or system indexes.
get_index_info	Fetches detailed information about a specific Splunk index; requires the index_name parameter.
indexes_and_sourcetypes	Returns a mapping of all indexes and their associated sourcetypes, useful for log and data exploration.
search_splunk	Executes a user-supplied Splunk search query, with optional parameters for time windows and maximum results.
list_saved_searches	Lists all saved search jobs in the Splunk instance, supporting automation and review of recurring queries.
list_kvstore_collections	Retrieves all existing KV store collections, allowing insight into Splunk's key-value storage structures.
create_kvstore_collection	Creates a new KV store collection in Splunk; requires the desired collection_name as a parameter.
delete_kvstore_collection	Deletes a specified KV store collection from the Splunk instance; requires collection_name parameter and appropriate permissions.