Jira MCP Server

An MCP server for accessing JIRA issue data stored in Snowflake.

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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 four main tools for interacting with the data:

  • list_jira_issues - Query and filter JIRA issues with various criteria
  • get_jira_issue_details - Get detailed information for a specific issue by key
  • get_jira_project_summary - Get statistics and summaries for all projects
  • list_jira_components - List and filter JIRA components with various criteria

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
  • JIRA_COMMENT_NON_PII - Issue comments (non-personally identifiable information)
  • JIRA_COMPONENT_RHAI - JIRA project components and their metadata

Note: Table names are expected to exist in your configured Snowflake database and schema.

Available Tools

1. List Issues (list_jira_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_jira_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
  • Comments (with comment body, creation/update timestamps, and role level)

3. Get Project Summary (get_jira_project_summary)

Generate statistics across all projects:

  • Total issue counts per project
  • Status distribution per project
  • Priority distribution per project
  • Overall statistics

4. List Components (list_jira_components)

Query and filter JIRA components with optional criteria:

  • Project filtering - Filter by project ID (e.g., '12325621')
  • Archived filtering - Filter by archived status ('Y' or 'N')
  • Deleted filtering - Filter by deleted status ('Y' or 'N')
  • Text search - Search in component name and description fields
  • Result limiting - Control number of results returned (default: 50)

Returns component information including:

  • Component ID, project, name, and description
  • Component URL and assignee details
  • Archived and deleted status
  • Lead information and assignee type
  • Last sync timestamp

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 3.10+
  • UV (Python package manager)
  • 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:

Connection Method

  • SNOWFLAKE_CONNECTION_METHOD - Connection method to use
    • Values: api (REST API) or connector (snowflake-connector-python)
    • Default: api

REST API Method (Default)

When using SNOWFLAKE_CONNECTION_METHOD=api:

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

Connector Method (Service Account Support)

When using SNOWFLAKE_CONNECTION_METHOD=connector:

Required for All Methods

  • SNOWFLAKE_ACCOUNT - Snowflake account identifier (e.g., your-account.snowflakecomputing.com)
  • SNOWFLAKE_DATABASE - Snowflake database name containing your JIRA data
  • SNOWFLAKE_SCHEMA - Snowflake schema name containing your JIRA tables
  • SNOWFLAKE_WAREHOUSE - Snowflake warehouse name

Authentication Methods

Private Key Authentication (Recommended for Service Accounts)

  • SNOWFLAKE_AUTHENTICATOR - Set to snowflake_jwt
  • SNOWFLAKE_USER - Snowflake username that has the public key registered
  • SNOWFLAKE_PRIVATE_KEY_FILE - Path to private key file (PKCS#8 format)
  • SNOWFLAKE_PRIVATE_KEY_FILE_PWD - Private key password (optional, if key is encrypted)

Username/Password Authentication

  • SNOWFLAKE_AUTHENTICATOR - Set to snowflake (default)
  • SNOWFLAKE_USER - Snowflake username
  • SNOWFLAKE_PASSWORD - Snowflake password

OAuth Client Credentials

  • SNOWFLAKE_AUTHENTICATOR - Set to oauth_client_credentials
  • SNOWFLAKE_OAUTH_CLIENT_ID - OAuth client ID
  • SNOWFLAKE_OAUTH_CLIENT_SECRET - OAuth client secret
  • SNOWFLAKE_OAUTH_TOKEN_URL - OAuth token URL (optional)

OAuth Token

  • SNOWFLAKE_AUTHENTICATOR - Set to oauth
  • SNOWFLAKE_TOKEN - OAuth access token

Optional

  • SNOWFLAKE_ROLE - Snowflake role to use (optional)

General Configuration

  • 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

Private Key Setup Example

To set up private key authentication:

  1. Generate RSA key pair:

    # Generate private key
openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8

# Generate public key
openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub

  2. Register public key with Snowflake user:

    ALTER USER your_service_account SET RSA_PUBLIC_KEY='MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA...';

  3. Set environment variables:

    export SNOWFLAKE_CONNECTION_METHOD=connector
export SNOWFLAKE_AUTHENTICATOR=snowflake_jwt
export SNOWFLAKE_ACCOUNT=your-account.snowflakecomputing.com
export SNOWFLAKE_USER=your_service_account
export SNOWFLAKE_PRIVATE_KEY_FILE=/path/to/rsa_key.p8
export SNOWFLAKE_DATABASE=your_database
export SNOWFLAKE_SCHEMA=your_schema
export SNOWFLAKE_WAREHOUSE=your_warehouse
export SNOWFLAKE_ROLE=your_role

Installation & Setup

Migration from pip to UV

This project has been updated to use UV for dependency management. If you have an existing setup:

  1. Remove your old virtual environment:

    rm -rf venv/

  2. Install UV if you haven't already (see Local Development section below)

  3. Install dependencies with UV:

    uv sync

Local Development

  1. Clone the repository:
git clone <repository-url>
cd jira-mcp-snowflake
  1. Install UV if you haven't already:
# On macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or via pip
pip install uv
  1. Install dependencies:
uv sync

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

  2. Run the server:

uv run python src/mcp_server.py

Using Makefile Targets

For convenience, several Makefile targets are available to streamline development tasks:

Development Setup

# Install dependencies including dev packages
make uv_sync_dev

Testing and Quality Assurance

# Run linting (flake8)
make lint

# Run tests with coverage
make pytest

# Run both linting and tests
make test

Building

# Build container image with Podman
make build

Note: On macOS, you may need to install a newer version of make via Homebrew:

brew install make

Container Deployment

Building locally

To build the container image locally using Podman, run:

podman build -t localhost/jira-mcp-snowflake:latest .

This will create a local image named jira-mcp-snowflake:latest that you can use to run the server. The container now uses UV for fast dependency management.

Running with Podman or Docker

Example 1: REST API with Token

{
  "mcpServers": {
    "jira-mcp-snowflake": {
      "command": "podman",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "SNOWFLAKE_CONNECTION_METHOD=api",
        "-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"
      ]
    }
  }
}

Example 2: Private Key Authentication (Service Account)

{
  "mcpServers": {
    "jira-mcp-snowflake": {
      "command": "podman",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v", "/path/to/your/rsa_key.p8:/app/rsa_key.p8:ro",
        "-e", "SNOWFLAKE_CONNECTION_METHOD=connector",
        "-e", "SNOWFLAKE_AUTHENTICATOR=snowflake_jwt",
        "-e", "SNOWFLAKE_ACCOUNT=your-account.snowflakecomputing.com",
        "-e", "SNOWFLAKE_USER=your_service_account",
        "-e", "SNOWFLAKE_PRIVATE_KEY_FILE=/app/rsa_key.p8",
        "-e", "SNOWFLAKE_DATABASE=your_database_name",
        "-e", "SNOWFLAKE_SCHEMA=your_schema_name",
        "-e", "SNOWFLAKE_WAREHOUSE=your_warehouse_name",
        "-e", "SNOWFLAKE_ROLE=your_role_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_jira_issues(project="SMQE", limit=10)

Search Issues by Text

# Search for issues containing "authentication" in summary or description
result = await list_jira_issues(search_text="authentication", limit=20)

Get Specific Issue Details

# Get detailed information for a specific issue
result = await get_jira_issue_details(issue_key="SMQE-1280")

Get Project Overview

# Get statistics for all projects
result = await get_jira_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

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