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

• 🚀 FastAPI: Built on a modern, fast web framework

• 🤖 MCP: Implements the Model Context Protocol for AI integration

• 🔍 Comprehensive DCI API: Full access to DCI components, jobs, files, pipelines, products, teams, and topics

• 🔧 Smart PR Detection: Advanced PR build finder that analyzes job URLs and metadata

• 🔐 DCI Integration: Native DCI API support with authentication

• 📝 Easy Configuration: Support for .env files for simple setup

• ✅ Code Quality: Comprehensive pre-commit checks and linting

• 📊 Google Drive Integration: Convert DCI reports to Google Docs with rich formatting

• 🎫 Jira Integration: Collect comprehensive ticket data from Jira with comments and changelog

• 🐙 GitHub Integration: Search issues and pull requests using GitHub's powerful search API

• 🔴 Red Hat Support Case Integration: Access Red Hat support case data from the Customer Portal

Installation

# Clone the repository
git clone https://github.com/redhat-community-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"
    }
  }
}

Claude Desktop (~/.claude.json)

{
  "mcpServers": {
    "dci": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/dci-mcp-server", "main.py"],
      "env": {
        "DCI_CLIENT_ID": "your-client-id",
        "DCI_API_SECRET": "your-secret",
        "DCI_CS_URL": "https://api.distributed-ci.io"
      }
    }
  }
}

Claude CLI (~/.claude.json)

• close the claude cli

• include the block above in the ~/.claude.json file

• start a claude session from the /path/to/dci-mcp-server/ directory

• verify MCP is connected with /mcp

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 prompts 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.

• /dci/quarterly <remoteci name/id> conducts a comprehensive quarterly analysis (last 3 months) with statistics about pipelines, topics, failure rates, trends, and component usage. Uses pagination and caching to handle large datasets. Report stored at /tmp/dci/<remoteci>/quarterly/<date-range>/report.md.

Google Drive Integration

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

Features

• 📄 Markdown to Google Docs: Convert markdown content to properly formatted Google Docs

• 📊 DCI Report Conversion: Specialized tools for converting DCI weekly/biweekly reports

• 🎨 Rich Formatting: Support for tables, code blocks, headers, lists, and links

• 🔐 OAuth2 Authentication: Secure authentication with Google Drive API

• 📁 Folder Organization: Option to organize documents in specific Google Drive folders

Setup

To use Google Drive features, follow the Google Drive Setup Guide 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

• 🎫 Ticket Data Collection: Retrieve comprehensive ticket information including summary, description, status, and dates

• 💬 Comments Analysis: Get up to 50 recent comments with author and timestamp information

• 📝 Changelog Tracking: Access complete ticket history and field changes

• 🔍 JQL Search: Search tickets using Jira Query Language (JQL)

• 📊 Project Information: Get project details and metadata

• 🔗 DCI Integration: Seamlessly extract Jira tickets from DCI job comments

• ✏️ Write Operations (opt-in): Create tickets, update fields, add comments, and transition status

Setup

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

Quick Setup:

1. Get your Jira API token from https://id.atlassian.com/manage-profile/security/api-tokens

2. Set environment variables in your .env file:

   JIRA_API_TOKEN=your_jira_api_token_here
   JIRA_EMAIL=you@redhat.com
   JIRA_URL=https://redhat.atlassian.net
   # Optional: enable write operations (create/update tickets, add comments)
   JIRA_WRITE_ENABLED=true

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'])

GitHub Integration

The server includes GitHub integration to search for issues and pull requests and retrieve detailed information about them.

Features

• 🔍 Issue & PR Search: Search using GitHub's powerful query syntax

• 📊 Comprehensive Data: Get detailed information including comments, labels, assignees, and more

• 🔀 Pull Request Metadata: Access PR-specific data like merge status, branch info, and file changes

• 📈 Repository Information: Retrieve repository metadata and statistics

• 🔐 Token Authentication: Secure authentication with GitHub personal access tokens

Setup

Quick Setup:

1. Get your GitHub personal access token from https://github.com/settings/tokens

2. Create a new token (classic) with repo scope (for private repos) or public_repo (for public repos only)

3. Set environment variable in your .env file:

   GITHUB_TOKEN=your_github_token_here

Red Hat Support Case Integration

The server includes Red Hat Support Case integration to retrieve case data from the Red Hat Customer Portal.

Features

• 📋 Case Data Retrieval: Get comprehensive case information including summary, status, severity, and product details

• 💬 Comments: Access case comments and communication history

• 🐛 Linked Bugs: View Bugzilla bugs linked to the case

• 📢 Errata/Advisory Details: Retrieve errata information including CVEs, affected products, and references

• 🔐 Offline Token Authentication: Secure authentication using Red Hat API offline tokens

Setup

Quick Setup:

1. Get your offline token from https://access.redhat.com/management/api

2. Set environment variable in your .env file:

   OFFLINE_TOKEN=your_offline_token_here

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

Enabled by default. Set DATE_TOOLS_ENABLED=false to disable.

• 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

• search_jira_child_tickets(parent_jql, child_jql, ...): Traverse a 2-level Jira hierarchy (e.g. TELCOSTRAT → Epics → Stories) in a single call, returning leaf tickets with full ancestry info

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

Jira Write Tools

• create_jira_ticket(project_key, summary, ...): Create a new Jira ticket with optional description, issue type, priority, labels, components, and assignee

• update_jira_ticket(ticket_key, ...): Update ticket fields (summary, description, priority, labels, components, assignee) or transition status

• add_jira_comment(ticket_key, body): Add a comment to a ticket

• list_jira_transitions(ticket_key): List available workflow transitions for a ticket

Note: Jira write tools require both JIRA_API_TOKEN and JIRA_WRITE_ENABLED=true environment variables.

GitHub Tools

• search_github_issues(query, max_results): Search issues and pull requests using GitHub search query syntax

• get_github_issue(repo, issue_number, max_comments): Get comprehensive issue/PR data including comments and PR-specific information

• get_github_repository_info(repo): Get repository information and statistics

• get_github_pr_diff(repo, pull_number, max_files): Get the diff/patch for a pull request with per-file unified diffs

• get_github_pr_checks(repo, pull_number): Get CI check runs and commit statuses for a pull request, including status, conclusion, and links to each CI job

Note: GitHub tools require GITHUB_TOKEN environment variable to be set.

Support Case Tools

• get_support_case(case_number): Get Red Hat support case data including comments and linked Bugzilla bugs

• get_support_case_comments(case_number, start_date?, end_date?): Get comments for a case with optional date filtering

• list_support_case_attachments(case_number): List attachment metadata for a case

• get_errata(advisory_id): Get Red Hat errata/advisory details (RHSA, RHBA, RHEA)

Note: Support Case tools require OFFLINE_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 mcp_server/ -c pyproject.toml

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/
├── config.py             # Configuration and authentication
├── main.py               # Server entry point
├── services/             # DCI API services
│   ├── dci_base_service.py
│   ├── dci_component_service.py
│   ├── dci_job_service.py
│   ├── dci_file_service.py
│   ├── dci_log_service.py
│   ├── dci_pipeline_service.py
│   ├── dci_product_service.py
│   ├── dci_team_service.py
│   ├── dci_remoteci_service.py
│   ├── dci_topic_service.py
│   ├── google_drive_service.py
│   ├── jira_service.py
│   ├── github_service.py
│   └── support_case_service.py
├── promps/               # Templatized prompts
│   └── prompts.py
├── tools/                # MCP tools
│   ├── component_tools.py
│   ├── date_tools.py
│   ├── job_tools.py
│   ├── file_tools.py
│   ├── google_drive_tools.py
│   ├── jira_tools.py
│   ├── jira_write_tools.py
│   ├── github_tools.py
│   ├── support_case_tools.py
│   └── log_tools.py
└── utils/                # Utility functions
    └── http_client.py

Testing

# Run all checks (format, lint, tests + evals)
bash scripts/run-checks.sh

# Run evals only
uv run pytest -m eval -v

# Run evals with a different model
EVAL_MODEL=haiku uv run pytest -m eval -v

Eval tests use claude -p to verify that Claude selects the correct MCP tools for natural language prompts. Each eval case is automatically skipped if its required credentials (DCI, Jira, GitHub, etc.) are not configured in .env.

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