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

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

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 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

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A Model Context Protocol server that provides access to JIRA issue data stored in CSV files, enabling AI assistants to query, filter, and analyze JIRA issues through standardized tools.

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