Jira MCP Server

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

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:

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

Register public key with Snowflake user:

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

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:

Remove your old virtual environment:
```
rm -rf venv/
```
Install UV if you haven't already (see Local Development section below)
Install dependencies with UV:
```
uv sync
```

Local Development

Clone the repository:

git clone <repository-url>
cd jira-mcp-snowflake

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

Install dependencies:

uv sync

Set up environment variables (see Environment Variables section above)
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:

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

Jira MCP Server

Jira MCP Server

Overview

Features

Data Sources

Available Tools

1. List Issues (list_jira_issues)

2. Get Issue Details (get_jira_issue_details)

3. Get Project Summary (get_jira_project_summary)

4. Get Issue Links (get_jira_issue_links)

5. Get Issues by Sprint (get_jira_issues_by_sprint)

Monitoring & Metrics

Prerequisites

Architecture

Environment Variables

Connection Method

REST API Method (Default)

Required

Connector Method (Service Account Support)

Required for All Methods

Authentication Methods

Optional

General Configuration

Private Key Setup Example

Installation & Setup

Migration from pip to UV

Local Development

Using Makefile Targets

Development Setup

Testing and Quality Assurance

Building

Container Deployment

Building locally

Running with Podman or Docker

Connecting to a remote instance

VS Code Continue Integration

Usage Examples

Query Issues by Project

Search Issues by Text

Filter Issues by Component

Filter Issues by Version

Filter Issues by Date

Get Specific Issue Details

Get Issue Links

Get Issues by Sprint

Get Project Overview

Monitoring

Available Metrics

Data Privacy

Security Considerations

Dependencies

Development

Code Structure

Adding New Tools

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1. List Issues (`list_jira_issues`)

2. Get Issue Details (`get_jira_issue_details`)

3. Get Project Summary (`get_jira_project_summary`)

4. Get Issue Links (`get_jira_issue_links`)

5. Get Issues by Sprint (`get_jira_issues_by_sprint`)