Jira MCP Server

# Jira MCP Server A Model Context Protocol (MCP) server that provides access to JIRA issue data stored in Snowflake. This server enables AI assistants to query, filter, and analyze JIRA issues through a standardized interface. ## Overview This MCP server connects to Snowflake to query JIRA data and provides three main tools for interacting with the data: - **`list_issues`** - Query and filter JIRA issues with various criteria - **`get_issue_details`** - Get detailed information for a specific issue by key - **`get_project_summary`** - Get statistics and summaries for all projects ## Features ### Data Sources The server connects to Snowflake and queries the following tables: - `JIRA_ISSUE_NON_PII` - Main issue data (non-personally identifiable information) - `JIRA_LABEL_RHAI` - Issue labels and tags **Note**: Table names are expected to exist in your configured Snowflake database and schema. ### Available Tools #### 1. List Issues (`list_issues`) Query JIRA issues with optional filtering: - **Project filtering** - Filter by project key (e.g., 'SMQE', 'OSIM') - **Issue type filtering** - Filter by issue type ID - **Status filtering** - Filter by issue status ID - **Priority filtering** - Filter by priority ID - **Text search** - Search in summary and description fields - **Result limiting** - Control number of results returned (default: 50) #### 2. Get Issue Details (`get_issue_details`) Retrieve comprehensive information for a specific JIRA issue by its key (e.g., 'SMQE-1280'), including: - Basic issue information (summary, description, status, priority) - Timestamps (created, updated, due date, resolution date) - Time tracking (original estimate, current estimate, time spent) - Metadata (votes, watches, environment, components) - Associated labels #### 3. Get Project Summary (`get_project_summary`) Generate statistics across all projects: - Total issue counts per project - Status distribution per project - Priority distribution per project - Overall statistics ### Monitoring & Metrics The server includes optional Prometheus metrics support for monitoring: - **Tool usage tracking** - Track calls to each MCP tool with success/error rates and duration - **Snowflake query monitoring** - Monitor database query performance and success rates - **Connection tracking** - Track active MCP connections - **HTTP endpoints** - `/metrics` for Prometheus scraping and `/health` for health checks ## Prerequisites - Python 3.8+ - Podman or Docker - Access to Snowflake with appropriate credentials ## Architecture The codebase is organized into modular components in the `src/` directory: - **`src/mcp_server.py`** - Main server entry point and MCP initialization - **`src/config.py`** - Configuration management and environment variable handling - **`src/database.py`** - Snowflake database connection and query execution - **`src/tools.py`** - MCP tool implementations and business logic - **`src/metrics.py`** - Optional Prometheus metrics collection and HTTP server ## Environment Variables The following environment variables are used to configure the Snowflake connection: ### Required - **`SNOWFLAKE_TOKEN`** - Your Snowflake authentication token (Bearer token) - **`SNOWFLAKE_BASE_URL`** - Snowflake API base URL (e.g., `https://your-account.snowflakecomputing.com/api/v2`) - **`SNOWFLAKE_DATABASE`** - Snowflake database name containing your JIRA data - **`SNOWFLAKE_SCHEMA`** - Snowflake schema name containing your JIRA tables ### Optional - **`MCP_TRANSPORT`** - Transport protocol for MCP communication - Default: `stdio` - **`ENABLE_METRICS`** - Enable Prometheus metrics collection - Default: `false` - **`METRICS_PORT`** - Port for metrics HTTP server - Default: `8000` ## Installation & Setup ### Local Development 1. Clone the repository: ```bash git clone <repository-url> cd jira-mcp-snowflake ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Set up environment variables (see Environment Variables section above) 4. Run the server: ```bash python src/mcp_server.py ``` ### Container Deployment ## Building locally To build the container image locally using Podman, run: ```sh podman build -t jira-mcp-snowflake:latest . ``` This will create a local image named `jira-mcp-snowflake:latest` that you can use to run the server. ## Running with Podman or Docker Example configuration for running with Podman: ```json { "mcpServers": { "jira-mcp-snowflake": { "command": "podman", "args": [ "run", "-i", "--rm", "-e", "SNOWFLAKE_TOKEN=your_token_here", "-e", "SNOWFLAKE_BASE_URL=https://your-account.snowflakecomputing.com/api/v2", "-e", "SNOWFLAKE_DATABASE=your_database_name", "-e", "SNOWFLAKE_SCHEMA=your_schema_name", "-e", "MCP_TRANSPORT=stdio", "-e", "ENABLE_METRICS=true", "-e", "METRICS_PORT=8000", "localhost/jira-mcp-snowflake:latest" ] } } } ``` Then access metrics at: `http://localhost:8000/metrics` ## Connecting to a remote instance Example configuration for connecting to a remote instance: ```json { "mcpServers": { "jira-mcp-snowflake": { "url": "https://jira-mcp-snowflake.example.com/sse", "headers": { "X-Snowflake-Token": "your_token_here" } } } } ``` ### VS Code Continue Integration Example configuration to add to VS Code Continue: ```json { "experimental": { "modelContextProtocolServers": [ { "name": "jira-mcp-snowflake", "transport": { "type": "stdio", "command": "podman", "args": [ "run", "-i", "--rm", "-e", "SNOWFLAKE_TOKEN=your_token_here", "-e", "SNOWFLAKE_BASE_URL=https://your-account.snowflakecomputing.com/api/v2", "-e", "SNOWFLAKE_DATABASE=your_database_name", "-e", "SNOWFLAKE_SCHEMA=your_schema_name", "-e", "MCP_TRANSPORT=stdio", "-e", "ENABLE_METRICS=true", "-e", "METRICS_PORT=8000", "localhost/jira-mcp-snowflake:latest" ] } } ] } } ``` ## Usage Examples ### Query Issues by Project ```python # List all issues from the SMQE project result = await list_issues(project="SMQE", limit=10) ``` ### Search Issues by Text ```python # Search for issues containing "authentication" in summary or description result = await list_issues(search_text="authentication", limit=20) ``` ### Get Specific Issue Details ```python # Get detailed information for a specific issue result = await get_issue_details(issue_key="SMQE-1280") ``` ### Get Project Overview ```python # Get statistics for all projects result = await get_project_summary() ``` ## Monitoring When metrics are enabled, the server provides the following monitoring endpoints: - **`/metrics`** - Prometheus metrics endpoint for scraping - **`/health`** - Health check endpoint returning JSON status ### Available Metrics - `mcp_tool_calls_total` - Counter of tool calls by tool name and status - `mcp_tool_call_duration_seconds` - Histogram of tool call durations - `mcp_active_connections` - Gauge of active MCP connections - `mcp_snowflake_queries_total` - Counter of Snowflake queries by status - `mcp_snowflake_query_duration_seconds` - Histogram of Snowflake query durations ## Data Privacy This server is designed to work with non-personally identifiable information (non-PII) data only. The Snowflake tables should contain sanitized data with any sensitive personal information removed. ## Security Considerations - **Environment Variables**: Store sensitive information like `SNOWFLAKE_TOKEN` in environment variables, never in code - **Token Security**: Ensure your Snowflake token is kept secure and rotated regularly - **Network Security**: Use HTTPS endpoints and secure network connections - **Access Control**: Follow principle of least privilege for Snowflake database access - **SQL Injection Prevention**: The server includes input sanitization to prevent SQL injection attacks ## Dependencies - `httpx` - HTTP client library for Snowflake API communication - `fastmcp` - Fast MCP server framework - `prometheus_client` - Prometheus metrics client (optional, for monitoring) ## Development ### Code Structure The project follows a modular architecture: ``` jira-mcp-snowflake/ ├── src/ │ ├── mcp_server.py # Main entry point │ ├── config.py # Configuration and environment variables │ ├── database.py # Snowflake database operations │ ├── tools.py # MCP tool implementations │ └── metrics.py # Prometheus metrics (optional) ├── requirements.txt # Python dependencies └── README.md # This file ``` ### Adding New Tools To add new MCP tools: 1. Add the tool function to `src/tools.py` 2. Decorate with `@mcp.tool()` and `@track_tool_usage("tool_name")` 3. Follow the existing patterns for error handling and logging 4. Update this README with documentation for the new tool