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 five 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 multiple issues by their keys
• `get_jira_project_summary` - Get statistics and summaries for all projects
• `get_jira_issue_links` - Get issue links for a specific JIRA issue by its key
• `get_jira_issues_by_sprint` - Get all JIRA issues in a specific sprint by sprint name

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
• `JIRA_NODEASSOCIATION_RHAI` - Associations between JIRA entities (issues, components, versions)
• `JIRA_PROJECTVERSION_NON_PII` - Project versions (fix versions and affected versions)
• `JIRA_ISSUELINK_RHAI` - Links between JIRA issues
• `JIRA_ISSUELINKTYPE_RHAI` - Types of issue links
• `JIRA_CUSTOMFIELDVALUE_NON_PII` - Custom field values (e.g., sprint information)
• `JIRA_SPRINT_RHAI` - Sprint data
• `JIRA_CHANGEGROUP_RHAI` - Change history groups
• `JIRA_CHANGEITEM_RHAI` - Individual change items (e.g., status changes)

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 keys filtering - Filter by specific issue keys (e.g., ['SMQE-1280', 'SMQE-1281'])
• 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
• Component filtering - Filter by component names (comma-separated, matches any)
• Version filtering - Filter by fixed version or affected version name
• Date filtering - Filter by creation, update, or resolution date within last N days
• Timeframe filtering - Filter issues where any date (created, updated, or resolved) is within last N days
• Result limiting - Control number of results returned (default: 50)

Returns issue information including:

• Basic issue information (summary, description, status, priority)
• Timestamps (created, updated, due date, resolution date)
• Metadata (votes, watches, environment, components)
• Associated labels and links
• Fixed and affected versions

2. Get Issue Details (`get_jira_issue_details`)

Retrieve comprehensive information for multiple JIRA issues by their keys (e.g., ['SMQE-1280', 'SMQE-1281']), 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, workflow ID, security, archived status)
• Associated labels
• Comments (with comment body, creation/update timestamps, and role level)
• Issue links (inward and outward)
• Status change history
• Fixed and affected versions

Returns a dictionary with:

• `found_issues` - Dictionary of found issues keyed by issue key
• `not_found` - List of issue keys that were not found
• `total_found` - Number of issues found
• `total_requested` - Number of issues requested

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. Get Issue Links (`get_jira_issue_links`)

Get issue links for a specific JIRA issue by its key (e.g., 'SMQE-1280'):

• Issue links - Relationships to other issues (blocks, is blocked by, relates to, etc.)
• Link direction - Indicates if the link is inward or outward
• Linked issue details - Information about the linked issue

Returns information including:

• Issue key and ID
• List of all issue links with link type and direction
• Total count of links

5. Get Issues by Sprint (`get_jira_issues_by_sprint`)

Get all JIRA issues in a specific sprint by sprint name:

• Sprint filtering - Filter by sprint name (e.g., 'Sprint 256')
• Project filtering - Optional filter by project key (e.g., 'SMQE', 'OSIM')
• Result limiting - Control number of results returned (default: 50)

Returns issue information including:

• All standard issue fields (same as `list_jira_issues`)
• Sprint ID and sprint name
• Associated labels and links
• Fixed and affected versions

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 
cd jira-mcp-snowflake

2. 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

3. Install dependencies:

uv sync

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

5. 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)

Filter Issues by Component

# Find issues in specific components
result = await list_jira_issues(components="Security,Authentication", limit=20)

Filter Issues by Version

# Find issues with a specific fixed version
result = await list_jira_issues(fixed_version="2.5.0", limit=20)

Filter Issues by Date

# Find issues created in the last 7 days
result = await list_jira_issues(created_days=7, limit=20)

# Find issues updated in the last 30 days
result = await list_jira_issues(updated_days=30, limit=50)

Get Specific Issue Details

# Get detailed information for multiple issues
result = await get_jira_issue_details(issue_keys=["SMQE-1280", "SMQE-1281"])

# Access the results
for issue_key, issue_data in result["found_issues"].items():
    print(f"Issue: {issue_key}")
    print(f"Summary: {issue_data['summary']}")
    print(f"Status: {issue_data['status']}")
    print(f"Labels: {issue_data['labels']}")
    print(f"Comments: {len(issue_data['comments'])}")

Get Issue Links

# Get all issue links for a specific issue
result = await get_jira_issue_links(issue_key="SMQE-1280")

# Access the links
print(f"Total links: {result['total_links']}")
for link in result['links']:
    print(f"Link type: {link['link_type']}")
    print(f"Direction: {link['direction']}")
    print(f"Linked issue: {link['linked_issue_key']}")

Get Issues by Sprint

# Get all issues in a specific sprint
result = await get_jira_issues_by_sprint(sprint_name="Sprint 256", limit=50)

# Get issues in a sprint for a specific project
result = await get_jira_issues_by_sprint(
    sprint_name="Sprint 256",
    project="SMQE",
    limit=50
)

# Access the results
print(f"Sprint: {result['sprint_name']}")
print(f"Total issues: {result['total_returned']}")
for issue in result['issues']:
    print(f"Issue: {issue['key']} - {issue['summary']}")
    print(f"Status: {issue['status']}")

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