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

Related MCP server: Python Jira MCP Server

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:

2. Install dependencies:

3. Set up environment variables (see Environment Variables section above)

4. Run the server:

Container Deployment

Building locally

To build the container image locally using Podman, run:

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:

Then access metrics at: http://localhost:8000/metrics

Connecting to a remote instance

Example configuration for connecting to a remote instance:

VS Code Continue Integration

Example configuration to add to VS Code Continue:

Usage Examples

Query Issues by Project

Search Issues by Text

Get Specific Issue Details

Get Project Overview

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:

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