Splunk MCP Archived

MCP Server generated by mcp.ag2.ai

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "rigzindorje-splunk-mcp-archived": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{\"openapiUrl\": \"https://...\"}",
        "SECURITY": "API_KEY=YOUR_API_KEY",
        "CONFIG_PATH": "mcp_server/mcp_config.json"
      }
    }
  }
}

An MCP (Model Context Protocol) Server exposes a defined API surface described by an OpenAPI URL and transports the data using flexible I/O channels. It lets you connect clients that consume and interact with the API through either a direct local process or remote transport, enabling rapid integration and testing against a consistent protocol.

How to use

You run the server locally and connect your MCP client to it using the stdio transport by default. Start the server, then initiate your MCP client to communicate through the chosen transport. The server supports multiple transports, including stdio, sse, and streamable-http, so you can pick the option that fits your environment. You can control security and configuration via environment variables or a JSON configuration file.

How to install

Prerequisites you need before installing the server are Python 3.9 or higher, as well as pip and the uv runner.

Install steps

# Step 1: Clone the repository
git clone <repository-url>
cd mcp_server

# Step 2: Install development dependencies
pip install -e ".[dev]"

# If you prefer using uv to run Python tooling, you can install editable dev dependencies with uv
uv pip install --editable ".[dev]"

Run and configure the server

You can start the MCP server using a Python script that supports various transport modes. The stdio transport is a straightforward option for local development.

python mcp_server/main.py stdio

Configuration and environment

Configure the server through environment variables or a JSON configuration file. The supported variables include:

CONFIG_PATH: Path to a JSON configuration file (for example, mcp_server/mcp_config.json).
CONFIG: A JSON string containing the configuration.
SECURITY: Environment variables for security parameters (for example, API keys).

Building and publishing

The project uses Hatch for building and publishing. Build the project to generate distributables, then publish to your package registry as needed.

Notes on transport modes

This server supports different transport modes such as stdio, sse, and streamable-http. Choose the mode that fits your debugging, testing, or production needs. The stdio mode runs as a local process and is convenient for development and automated tests.

Testing and quality tooling

Development tooling includes linting, static type checking, and tests. Linting checks, formatting, static analysis, and test execution help maintain code quality as you work with the MCP server.

Security considerations

Security-related configuration is controlled through environment variables. Ensure you set API keys or other secrets via the SECURITY variable or a configuration JSON so that clients can securely access the MCP surface.

Examples and practical notes

When you want to connect a client, instantiate the MCP client to communicate with the server over the chosen transport. Use the stdio path for quick local testing, or switch to a streaming HTTP or SSE transport for more complex integration scenarios.