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 criteriaget_issue_details- Get detailed information for a specific issue by keyget_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 -
/metricsfor Prometheus scraping and/healthfor 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 initializationsrc/config.py- Configuration management and environment variable handlingsrc/database.py- Snowflake database connection and query executionsrc/tools.py- MCP tool implementations and business logicsrc/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 dataSNOWFLAKE_SCHEMA- Snowflake schema name containing your JIRA tables
Optional
MCP_TRANSPORT- Transport protocol for MCP communication- Default:
stdio
- Default:
ENABLE_METRICS- Enable Prometheus metrics collection- Default:
false
- Default:
METRICS_PORT- Port for metrics HTTP server- Default:
8000
- Default:
Installation & Setup
Local Development
- Clone the repository:
git clone <repository-url>
cd jira-mcp-snowflake
- Install dependencies:
pip install -r requirements.txt
-
Set up environment variables (see Environment Variables section above)
-
Run the server:
python src/mcp_server.py
Container Deployment
Building locally
To build the container image locally using Podman, run:
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:
{
"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:
{
"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:
{
"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
# List all issues from the SMQE project
result = await list_issues(project="SMQE", limit=10)
Search Issues by Text
# Search for issues containing "authentication" in summary or description
result = await list_issues(search_text="authentication", limit=20)
Get Specific Issue Details
# Get detailed information for a specific issue
result = await get_issue_details(issue_key="SMQE-1280")
Get Project Overview
# 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 statusmcp_tool_call_duration_seconds- Histogram of tool call durationsmcp_active_connections- Gauge of active MCP connectionsmcp_snowflake_queries_total- Counter of Snowflake queries by statusmcp_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_TOKENin 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 communicationfastmcp- Fast MCP server frameworkprometheus_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:
- Add the tool function to
src/tools.py - Decorate with
@mcp.tool()and@track_tool_usage("tool_name") - Follow the existing patterns for error handling and logging
- Update this README with documentation for the new tool