dci-mcp-server

redhat-community-ai-tools/dci-mcp-server

3.3

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The DCI MCP Server is a Model Context Protocol server designed to facilitate AI model interactions with the DCI API for data extraction and analysis.

Tools
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Resources
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Prompts
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DCI MCP Server

This project provides a Model Context Protocol (MCP) server adapted for the DCI API. It allows AI models to interact with DCI for comprehensive data extraction about DCI jobs, components, topics and files.

Features

  • šŸš€ FastAPI: Built on a modern, fast web framework
  • šŸ¤– MCP: Implements the Model Context Protocol for AI integration
  • šŸ” Comprehensive DCI API: Full access to DCI components, jobs, files, pipelines, products, teams, and topics
  • šŸ”§ Smart PR Detection: Advanced PR build finder that analyzes job URLs and metadata
  • šŸ” DCI Integration: Native DCI API support with authentication
  • šŸ“ Easy Configuration: Support for .env files for simple setup
  • āœ… Code Quality: Comprehensive pre-commit checks and linting
  • šŸ“Š Google Drive Integration: Convert DCI reports to Google Docs with rich formatting
  • šŸŽ« Jira Integration: Collect comprehensive ticket data from Jira with comments and changelog

Installation

# Clone the repository
git clone https://github.com/redhat-ai-tools/dci-mcp-server
cd dci-mcp-server

# Install dependencies
uv sync

# Activate virtual environment
source .venv/bin/activate

Configuration

The server supports multiple ways to configure DCI authentication:

Copy the example file and customize it:

cp env.example .env
# Edit .env with your DCI credentials

Example .env file:

# Method 1: API Key Authentication
DCI_CLIENT_ID=<client_type>/<client_id>
DCI_API_SECRET=<api_secret>

# Method 2: User ID/Password (alternative to API key)
# DCI_LOGIN=foo
# DCI_PASSWORD=bar

# Google Drive Integration (optional)
# GOOGLE_CREDENTIALS_PATH=credentials.json
# GOOGLE_TOKEN_PATH=token.json

MCP Configuration

Cursor IDE (stdio transport)

Add to your ~/.cursor/mcp.json:

{
  "mcpServers": {
    "dci": {
      "command": "uv",
      "args": ["run", "/path/to/dci-mcp-server/.venv/bin/python", "/path/to/dci-mcp-server/main.py"],
      "description": "MCP server for DCI integration"
    }
  }
}
Web-based Integration (SSE transport)

For web applications or services that need HTTP-based communication:

{
  "mcpServers": {
    "dci": {
      "url": "http://0.0.0.0:8000/sse/",
      "description": "MCP server for DCI integration with direct SSE",
      "env": {
        "MCP_TRANSPORT": "sse"
      }
    }
  }
}

SSE Endpoint: http://0.0.0.0:8000/sse/

Note: Make sure to start the SSE server separately with MCP_TRANSPORT=sse uv run main.py before using this configuration.

Prompts

You can then use to explore the DCI data.

There are also parameterized prompts defined in the MCP server:

  • /dci/rca <job id> conducts a Root Cause Analysis of the problem in the job. Storing the downloaded files under /tmp/dci/<job id> and generating a report at /tmp/dci/rca-<job id>.md.
  • /dci/weekly <team name/id or remoteci name/id> conducts a report for the last 7 days stored at /tmp/dci.
  • /dci/biweekly <team name/id or remoteci name/id> conducts a report for the last 14 days stored at /tmp/dci.

Google Drive Integration

The server includes Google Drive integration to convert DCI reports and markdown content to Google Docs with rich formatting support.

Features

  • šŸ“„ Markdown to Google Docs: Convert markdown content to properly formatted Google Docs
  • šŸ“Š DCI Report Conversion: Specialized tools for converting DCI weekly/biweekly reports
  • šŸŽØ Rich Formatting: Support for tables, code blocks, headers, lists, and links
  • šŸ” OAuth2 Authentication: Secure authentication with Google Drive API
  • šŸ“ Folder Organization: Option to organize documents in specific Google Drive folders

Setup

To use Google Drive features, follow the for detailed configuration instructions.

Quick Setup:

  1. Set up Google Cloud Project and enable Google Drive API
  2. Download OAuth2 credentials and save as credentials.json
  3. Initialize the service: uv run python -c "from mcp_server.services.google_drive_service import GoogleDriveService; GoogleDriveService()"
  4. Complete browser authentication when prompted

Usage Examples

# Convert a DCI report to Google Doc in a specific folder by name
result = await convert_dci_report_to_google_doc(
    report_path="/tmp/dci/the_weekly_report_2025-09-09.md",
    doc_title="The Weekly Report - September 2025",
    folder_name="DCI Reports"
)

# Create a Google Doc from markdown content in a folder by ID
result = await create_google_doc_from_markdown(
    markdown_content="# My Report\n\nThis is a **test** document.",
    doc_title="My Custom Report",
    folder_id="1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms"
)

# Create a Google Doc from a file in a folder by name
result = await create_google_doc_from_file(
    file_path="/path/to/report.md",
    doc_title="My Report",
    folder_name="Project Documents"
)

Jira Integration

The server includes Jira integration to collect comprehensive ticket data from Red Hat Jira, including comments and changelog information.

Features

  • šŸŽ« Ticket Data Collection: Retrieve comprehensive ticket information including summary, description, status, and dates
  • šŸ’¬ Comments Analysis: Get up to 50 recent comments with author and timestamp information
  • šŸ“ Changelog Tracking: Access complete ticket history and field changes
  • šŸ” JQL Search: Search tickets using Jira Query Language (JQL)
  • šŸ“Š Project Information: Get project details and metadata
  • šŸ”— DCI Integration: Seamlessly extract Jira tickets from DCI job comments

Setup

To use Jira features, follow the for detailed configuration instructions.

Quick Setup:

  1. Get your Jira API token from https://issues.redhat.com/secure/ViewProfile.jspa
  2. Set environment variables in your .env file: 
    JIRA_API_TOKEN=your_jira_api_token_here
JIRA_URL=https://issues.redhat.com

Usage Examples

# Get comprehensive ticket data with comments
ticket_data = await get_jira_ticket("CILAB-1234", max_comments=10)

# Search for tickets using JQL
open_tickets = await search_jira_tickets("project = CILAB AND status = Open")

# Get project information
project_info = await get_jira_project_info("CILAB")

# Extract Jira tickets from DCI job comments
jobs_with_tickets = await search_dci_jobs("comment=~'.*CILAB.*'")
for job in jobs_with_tickets:
    if job.get('comment'):
        ticket_data = await get_jira_ticket(job['comment'])

Available Tools exposed by the MCP server

The server provides tools for interacting with DCI API components:

Component Tools

  • query_dci_components(query, limit, offset, sort, fields): Query components with advanced query language and pagination

Date Tools

  • today(): Returns today's date in YYYY-MM-DD format.
  • now(): Returns current date and time in DCI compatible format (GMT).

Job Tools

  • search_dci_jobs(query, sort, limit, offset, fields): Search jobs with advanced query language and pagination

File Tools

  • download_dci_file(job_id, file_id, output_path): Download a file to local path

Google Drive Tools

  • create_google_doc_from_markdown(markdown_content, doc_title, folder_id, folder_name): Create a Google Doc from markdown content
  • create_google_doc_from_file(file_path, doc_title, folder_id, folder_name): Create a Google Doc from a markdown file
  • convert_dci_report_to_google_doc(report_path, doc_title, folder_id, folder_name): Convert a DCI report to Google Doc
  • list_google_docs(query, max_results): List Google Docs in your Drive

Note: For folder placement, you can use either folder_id (exact folder ID) or folder_name (searches for folder by name). Do not use both parameters together.

Jira Tools

  • get_jira_ticket(ticket_key, max_comments): Get comprehensive ticket data including comments and changelog
  • search_jira_tickets(jql, max_results): Search tickets using JQL (Jira Query Language)
  • get_jira_project_info(project_key): Get project information and metadata

Note: Jira tools require JIRA_API_TOKEN environment variable to be set.

Code Quality Checks

The project includes comprehensive code quality checks:

Manual Checks
# Run all checks
bash scripts/run-checks.sh

# Or run individual checks
./.venv/bin/python -m black --check .
./.venv/bin/python -m isort --check-only .
./.venv/bin/python -m ruff check .
./.venv/bin/python -m mypy mcp_server/
./.venv/bin/python -m bandit -r .
Pre-commit Hooks (Optional)
# Install pre-commit hooks
./.venv/bin/python -m pre_commit install

# Run pre-commit on all files
./.venv/bin/python -m pre_commit run --all-files

Development

Project Structure

mcp_server/
ā”œā”€ā”€ config.py             # Configuration and authentication
ā”œā”€ā”€ main.py               # Server entry point
ā”œā”€ā”€ services/             # DCI API services
ā”‚   ā”œā”€ā”€ dci_base_service.py
ā”‚   ā”œā”€ā”€ dci_component_service.py
ā”‚   ā”œā”€ā”€ dci_job_service.py
ā”‚   ā”œā”€ā”€ dci_file_service.py
ā”‚   ā”œā”€ā”€ dci_log_service.py
ā”‚   ā”œā”€ā”€ dci_pipeline_service.py
ā”‚   ā”œā”€ā”€ dci_product_service.py
ā”‚   ā”œā”€ā”€ dci_team_service.py
ā”‚   ā”œā”€ā”€ dci_remoteci_service.py
ā”‚   ā”œā”€ā”€ dci_topic_service.py
ā”‚   ā””ā”€ā”€ google_drive_service.py
ā”œā”€ā”€ promps/               # Templatized prompts
ā”‚   ā””ā”€ā”€ prompts.py
ā”œā”€ā”€ tools/                # MCP tools
ā”‚   ā”œā”€ā”€ component_tools.py
ā”‚   ā”œā”€ā”€ date_tools.py
ā”‚   ā”œā”€ā”€ job_tools.py
ā”‚   ā”œā”€ā”€ file_tools.py
ā”‚   ā”œā”€ā”€ google_drive_tools.py
ā”‚   ā””ā”€ā”€ log_tools.py
ā””ā”€ā”€ utils/                # Utility functions
    ā””ā”€ā”€ http_client.py

Adding New Tools

  1. Create a new service in mcp_server/services/ if needed
  2. Create a new tool file in mcp_server/tools/
  3. Register the tools in mcp_server/main.py
  4. Update this README with documentation