Swarmia MCP Server
Connect Swarmia's metrics and reports to MCP clients for advanced analytics.
Key Features
Use Cases
README
Swarmia MCP Server
A Model Context Protocol (MCP) server that provides access to Swarmia's Export API. This server allows you to fetch various metrics and reports from Swarmia including pull request metrics, DORA metrics, investment balance reports, software capitalization reports, and effort reporting.
Features
This MCP server provides access to the following Swarmia Export API endpoints:
- Pull Request Metrics - Cycle time, review rate, merge time, PRs in progress, etc.
- DORA Metrics - Deployment frequency, change lead time, change failure rate, mean time to recovery
- Investment Balance - Monthly FTE data and investment category breakdowns
- Software Capitalization Report - Employee contributions to capitalizable work
- Software Capitalization Employees - FTE effort breakdown by month for each employee
- Effort Reporting - Authors and their FTE for each issue in a given month
Prerequisites
- Python 3.8 or higher
- A Swarmia account with API access
- A Swarmia API token (obtain from Settings/API tokens in your Swarmia dashboard)
Integration with MCP Clients
To use this MCP server with your favourite MCP client (E.g. Claude, Cursor etc.):
- Ensure depedancies are installed
make install
- Add the following to your MCP configuration
{
"mcpServers": {
"swarmia": {
"command": "/path/to/swarmia-mcp/venv/bin/python3",
"args": ["/path/to/swarmia-mcp/swarmia_mcp_server.py"],
"env": {
"SWARMIA_API_TOKEN": "your_api_token_here"
}
}
}
}
-
Restart your client application
-
Ask for some metrics Example queries:
- "Analyze our team's pull request cycle time trends"
- "Get the software capitalization report for Q1 2024"
- "Show me effort reporting for last month"
Installation for Development
- Clone or download this repository
- Install the required dependencies and setup the project:
make install
- Set up your Swarmia API token as an environment variable:
export SWARMIA_API_TOKEN="your_api_token_here"
Quick Setup
For a complete setup including dependency installation and environment checks:
make setup
Usage
Running the MCP Server
To run the server:
make run
Or directly:
python3 swarmia_mcp_server.py
The server will start and listen for MCP client connections via stdio.
Available Tools
The server provides the following tools:
1. get_pull_request_metrics
Get pull request metrics for the organization.
Parameters:
timeframe(optional): Predefined timeframe (last_7_days,last_14_days,last_30_days, etc.)start_date(optional): Start date in YYYY-MM-DD format (alternative to timeframe)end_date(optional): End date in YYYY-MM-DD format (alternative to timeframe)timezone(optional): Timezone for data aggregation (default: UTC)
Returns: CSV data with columns including Start Date, End Date, Team, Cycle Time, Review Rate, Time to first review, PRs merged/week, Merge Time, PRs in progress, Contributors.
2. get_dora_metrics
Get DORA metrics for the organization.
Parameters:
timeframe(optional): Predefined timeframestart_date(optional): Start date in YYYY-MM-DD formatend_date(optional): End date in YYYY-MM-DD formattimezone(optional): Timezone for data aggregationapp(optional): Deployment application name(s), comma-separatedenvironment(optional): Deployment environment(s), comma-separated
Returns: CSV data with DORA metrics including Deployment Frequency, Change Lead Time, Average Time to Deploy, Change Failure Rate, Mean Time to Recovery, Deployment Count.
3. get_investment_balance
Get investment balance statistics using the Effort model.
Parameters:
start_date(required): First day of the month in YYYY-MM-DD formatend_date(required): Last day of the month in YYYY-MM-DD formattimezone(optional): Timezone for data aggregation
Returns: CSV data with investment categories, FTE months, relative percentages, and activity counts.
4. get_software_capitalization_report
Get software capitalization report with employee contributions.
Parameters:
start_date(required): First day of the start month in YYYY-MM-DD formatend_date(required): Last day of the end month in YYYY-MM-DD formattimezone(optional): Timezone for data aggregation
Returns: CSV data with employee details, capitalizable work, developer months, and additional context.
5. get_software_capitalization_employees
Get list of employees with FTE effort breakdown by month.
Parameters:
year(required): Year for the report (e.g., 2024)timezone(optional): Timezone for data aggregation
Returns: CSV data with employee details and monthly FTE breakdowns.
6. get_effort_reporting
Get effort reporting for authors and their FTE for each issue.
Parameters:
month(required): Month in YYYY-MM-DD format (first day of the month)timezone(optional): Timezone for data aggregationcustom_field(optional): Jira field ID to include as Custom field columngroup_by(optional): How FTE rows should be grouped (highestLevelIssue,lowestLevelIssue,customField)
Returns: CSV data with author details, FTE contributions, and issue information.
Configuration
Environment Variables
SWARMIA_API_TOKEN: Your Swarmia API token (required)
Timeframes
The following predefined timeframes are available:
last_7_dayslast_14_dayslast_30_dayslast_60_dayslast_90_dayslast_180_dayslast_365_days
Timezones
You can specify any timezone using tz database identifiers (e.g., America/New_York, Europe/London, Asia/Tokyo). The default is UTC.
API Reference
This MCP server is based on the Swarmia Export API documentation.
Base URL
https://app.swarmia.com/api/v0
Authentication
The server uses token-based authentication. Your API token is passed as a query parameter to all requests.
Error Handling
The server includes comprehensive error handling for:
- Missing or invalid API tokens
- HTTP request failures
- Invalid parameters
- API rate limiting
- Network connectivity issues
Logging
The server logs all activities at the INFO level. You can adjust the logging level by modifying the logging.basicConfig() call in the server code.
Development
Available Make Targets
The project includes a comprehensive Makefile with the following targets:
make help- Show available targets and help informationmake install- Install dependencies and setup the projectmake setup- Complete setup (install + environment checks)make test- Test the API connection and server functionalitymake run- Run the MCP servermake check-env- Check if required environment variables are setmake clean- Clean up temporary filesmake format- Format code with black (if available)make lint- Lint code with flake8 (if available)make type-check- Type check with mypy (if available)make quality- Run all quality checksmake info- Show project information
Testing
To test the server:
make test
Example Usage
Here's an example of how you might use this server with an MCP client:
# Example: Get pull request metrics for the last 30 days
result = await client.call_tool(
"get_pull_request_metrics",
{
"timeframe": "last_30_days",
"timezone": "America/New_York"
}
)
# Example: Get DORA metrics for a specific date range
result = await client.call_tool(
"get_dora_metrics",
{
"start_date": "2024-01-01",
"end_date": "2024-01-31",
"app": "my-app",
"environment": "production"
}
)
# Example: Get investment balance for January 2024
result = await client.call_tool(
"get_investment_balance",
{
"start_date": "2024-01-01",
"end_date": "2024-01-31"
}
)
Troubleshooting
Common Issues
-
"SWARMIA_API_TOKEN environment variable is required"
- Make sure you've set the
SWARMIA_API_TOKENenvironment variable - Verify your token is valid and has the necessary permissions
- Make sure you've set the
-
"API request failed with status 401"
- Your API token may be invalid or expired
- Check your token in the Swarmia dashboard
-
"API request failed with status 403"
- Your token may not have permission to access the requested data
- Contact your Swarmia administrator
-
"Request failed: Connection timeout"
- Check your internet connection
- Verify that
app.swarmia.comis accessible from your network
Getting Help
- Check the Swarmia Export API documentation
- Review the Swarmia API token settings in your dashboard
- Contact Swarmia support for API-related issues
Star History
Repository Owner
User
Repository Details
Programming Languages
Join Our Newsletter
Stay updated with the latest AI tools, news, and offers by subscribing to our weekly newsletter.
Related MCPs
Discover similar Model Context Protocol servers
MacOS Resource Monitor MCP Server
Lightweight MCP server for monitoring CPU, memory, and network usage on macOS.
MacOS Resource Monitor MCP Server provides real-time monitoring of system resources on macOS devices, exposing an MCP endpoint for integration with LLMs or other clients. It identifies resource-intensive processes across CPU, memory, and network, delivering structured JSON outputs. The server offers advanced filtering, sorting, and system overviews, assisting in performance analysis and bottleneck identification. Designed for seamless integration and lightweight system monitoring.
- ⭐ 16
- MCP
- Pratyay/mac-monitor-mcp
SonarQube MCP Server
Model Context Protocol server for AI access to SonarQube code quality metrics.
SonarQube MCP Server offers a Model Context Protocol (MCP) server that integrates with SonarQube, enabling AI assistants to access code quality metrics, issues, and analysis results programmatically. It supports retrieving detailed quality metrics, filtering issues, reviewing security hotspots, analyzing branches and pull requests, and monitoring project health. The server facilitates multi-project analysis, contextual code review, and improved assistant workflows through a standardized protocol.
- ⭐ 101
- MCP
- sapientpants/sonarqube-mcp-server
JMeter MCP Server
Execute and analyze JMeter tests via Model Context Protocol integration.
JMeter MCP Server enables execution and analysis of Apache JMeter tests through MCP-compatible clients. It provides command-line and programmatic tools for running JMeter tests in GUI and non-GUI modes, parsing and analyzing JTL result files, and generating detailed metrics and reports. Designed for integration with tools that follow the Model Context Protocol, it facilitates seamless performance testing workflows and actionable insights for test results.
- ⭐ 47
- MCP
- QAInsights/jmeter-mcp-server
Rootly MCP Server
Seamlessly integrate Rootly incident management into MCP-compatible editors.
Rootly MCP Server provides an MCP-compliant server to access and manage Rootly's incident management API from within editors like Cursor, Windsurf, and Claude. It enables context-rich workflows and tool generation based on Rootly’s OpenAPI specification, allowing users to resolve incidents directly within their development environment. The server supports flexible authentication and dynamic resource generation while ensuring smart pagination to optimize editor context windows.
- ⭐ 36
- MCP
- Rootly-AI-Labs/Rootly-MCP-server
Crypto Sentiment MCP Server
AI-accessible crypto sentiment data powered by Santiment.
Delivers cryptocurrency sentiment analysis, social volume metrics, trending topics, and social dominance statistics to AI agents via the Model Context Protocol. Provides standardized APIs for retrieving market mood, tracking trending words, and monitoring significant shifts in online discussions about crypto assets. Integrates with Santiment to leverage aggregated data from social media and news sources for in-depth market insights.
- ⭐ 40
- MCP
- kukapay/crypto-sentiment-mcp
OpsLevel MCP Server
Read-only MCP server for integrating OpsLevel data with AI tools.
OpsLevel MCP Server implements the Model Context Protocol to provide AI tools with a secure way to access and interact with OpsLevel account data. It supports read-only operations for a wide range of OpsLevel resources such as actions, campaigns, checks, components, documentation, domains, and more. The tool is compatible with popular environments including Claude Desktop and VS Code, enabling easy integration via configuration and API tokens. Installation options include Homebrew, Docker, and standalone binaries.
- ⭐ 8
- MCP
- OpsLevel/opslevel-mcp
Didn't find tool you were looking for?