Question 1

What is the PagerDuty MCP Server?

Accepted Answer

The PagerDuty MCP Server is a tool that allows Large Language Models (LLMs) to interact with the PagerDuty API. It provides structured inputs and outputs, enabling AI agents to automate incident management tasks.

Question 2

What are the benefits of using the PagerDuty MCP Server?

Accepted Answer

Key benefits include seamless LLM integration, automated incident response, context-aware automation, enhanced collaboration, improved uptime, and simplified workflows.

Question 3

How do I install the PagerDuty MCP Server?

Accepted Answer

You can install it from PyPI using `pip install pagerduty-mcp-server` or from source following the instructions in the documentation.

Question 4

What configuration is required to use the server?

Accepted Answer

You need to set your PagerDuty API key as an environment variable: `PAGERDUTY_API_KEY=your_api_key_here`.

Question 5

Can I use the server as a Goose Extension?

Accepted Answer

Yes, the documentation provides instructions on how to configure it as a Goose Extension.

Question 6

What is the response format of the API?

Accepted Answer

All API responses follow a consistent JSON format with metadata, a resource list (pluralized), and an optional error object.

Question 7

How does the server handle errors?

Accepted Answer

When an error occurs, the response includes an error object with a message and a code, providing information on the issue.

Question 8

Does the server handle rate limiting and pagination?

Accepted Answer

Yes, the server respects PagerDuty's rate limits and automatically handles pagination for you.

Question 9

What is the `current_user_context` parameter?

Accepted Answer

The `current_user_context` parameter filters results based on the current user's context. When set to `True`, you cannot use certain filter parameters like `user_ids`.

Question 10

How can I contribute to the project?

Accepted Answer

Contributions are welcome! The project uses Conventional Commits for automated releases. Refer to the documentation for guidelines on development, testing, and contribution.

Question 11

What types of tests are included in the project?

Accepted Answer

The project includes unit tests, integration tests, and parser tests, each marked with specific pytest markers.

Question 12

Where can I find more detailed information about the available tools?

Accepted Answer

You can find detailed information in the [Tool Documentation](./docs/tools.md) file, including parameters, return types, and example queries.

Question 13

What is UBOS, and how does it relate to the PagerDuty MCP Server?

Accepted Answer

UBOS is a full-stack AI Agent Development Platform. The PagerDuty MCP Server is available on the UBOS Asset Marketplace, making it easy to integrate with AI agent workflows.

Question 14

What are some example use cases for the PagerDuty MCP Server?

Accepted Answer

Use cases include intelligent alerting, automated incident triage, proactive incident prevention, AI-powered chatbots, and automated remediation.

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