> ## Documentation Index
> Fetch the complete documentation index at: https://docs.spurtest.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Connecting with Other MCP Tools

> Combine the Spur MCP with GitHub, Jira, Linear, and other tools to give your AI assistant richer context for testing.

The Spur MCP becomes more powerful when your AI assistant has access to other tools alongside it. By connecting MCPs for project management, source control, or communication, your agent can pull in context from across your workflow — and make smarter decisions about what to test, when to test it, and how to interpret results.

## GitHub

Connect a GitHub MCP so your agent can read pull requests, commits, and code changes — then match them to the right Spur tests.

<Frame>
  <img src="https://mintcdn.com/spur/wB1dCdgur0RTtos6/images/MCP/mcp_github.png?fit=max&auto=format&n=wB1dCdgur0RTtos6&q=85&s=0fe18911426edce11e232f6d1ce95c39" alt="Using the Spur MCP alongside GitHub" width="1466" height="712" data-path="images/MCP/mcp_github.png" />
</Frame>

### Example prompts

* *"Run any Spur tests that cover the files changed in this PR"*
* *"This commit broke checkout — find the relevant Spur test and show me what failed"*
* *"What Spur test coverage do I have for the code in PR #142?"*

### How it works

<Steps>
  <Step title="Agent reads the PR or commit">
    Your agent uses the GitHub MCP to fetch the changed files, commit messages, or PR description.
  </Step>

  <Step title="Agent matches changes to tests">
    Using the context from GitHub, the agent calls `list_tests` and `get_test_details` to find Spur tests that cover the affected areas.
  </Step>

  <Step title="Agent runs and reports">
    The agent runs the matching tests with `run_test`, then summarizes results — so you know whether your changes are safe before merging.
  </Step>
</Steps>

## Jira

Connect a Jira MCP so your agent can read tickets, acceptance criteria, and bug reports — then tie them directly to Spur test runs.

<Frame>
  <img src="https://mintcdn.com/spur/wB1dCdgur0RTtos6/images/MCP/mcp_jira_1.png?fit=max&auto=format&n=wB1dCdgur0RTtos6&q=85&s=61f2ced8c49d0c29682a99f4ae09551f" alt="Using the Spur MCP alongside Jira" width="1152" height="952" data-path="images/MCP/mcp_jira_1.png" />
</Frame>

### Example prompts

* *"Run the Spur test that covers Jira ticket SHOP-451"*
* *"This Jira bug says checkout is broken — find and run the relevant Spur tests"*
* *"Check if I have Spur test coverage for the acceptance criteria in SHOP-302"*

### How it works

<Steps>
  <Step title="Agent reads the Jira ticket">
    Your agent uses the Jira MCP to fetch the ticket summary, description, and acceptance criteria.

    <Frame>
      <img src="https://mintcdn.com/spur/wB1dCdgur0RTtos6/images/MCP/mcp_jira_2.png?fit=max&auto=format&n=wB1dCdgur0RTtos6&q=85&s=4890bfa1fc7658a9ebc797c44f3f7449" alt="Agent matching Jira ticket to Spur tests" width="1670" height="1134" data-path="images/MCP/mcp_jira_2.png" />
    </Frame>
  </Step>

  <Step title="Agent matches ticket to tests">
    The agent calls `list_tests` and `get_test_details` to find Spur tests that match the ticket scope, then presents them for you to run.

    <Frame>
      <img src="https://mintcdn.com/spur/wB1dCdgur0RTtos6/images/MCP/mcp_jira_3.png?fit=max&auto=format&n=wB1dCdgur0RTtos6&q=85&s=1b7167267faee51099506ac03b5e5f85" alt="Agent reporting Spur test results for a Jira ticket" width="1598" height="1248" data-path="images/MCP/mcp_jira_3.png" />
    </Frame>
  </Step>

  <Step title="Agent runs and reports">
    The agent runs the matching tests with `run_test`, then summarizes the results — so you know which acceptance criteria are passing and which need attention.
  </Step>
</Steps>

## Linear

Connect a Linear MCP so your agent can read issues, project context, and cycle priorities — then link them to your Spur test suite.

<Frame>
  <img src="https://mintcdn.com/spur/wB1dCdgur0RTtos6/images/MCP/mcp_linear.png?fit=max&auto=format&n=wB1dCdgur0RTtos6&q=85&s=6f78e4f783b6c71188b51c8a5cbd2642" alt="Using the Spur MCP alongside Linear" width="1576" height="950" data-path="images/MCP/mcp_linear.png" />
</Frame>

### Example prompts

* *"Run Spur tests related to the issues in the current sprint"*
* *"This Linear issue says the login flow is broken — find and run the matching Spur test"*
* *"What Spur test coverage do I have for the issues assigned to me?"*

### How it works

<Steps>
  <Step title="Agent reads the Linear issue">
    Your agent uses the Linear MCP to fetch issue details, labels, and project context.
  </Step>

  <Step title="Agent matches issues to tests">
    The agent calls `list_tests` and `get_test_details` to find Spur tests that align with the issue scope.
  </Step>

  <Step title="Agent runs and reports">
    The agent executes the relevant tests and reports back with results mapped to the Linear issue, so you can update the issue status with confidence.
  </Step>
</Steps>

## Other tools

Any MCP that gives your agent context about *what changed* or *what matters* pairs well with Spur. The pattern is always the same: the external tool provides context, and Spur provides the testing.

Examples of other MCPs that work well alongside Spur:

* **Slack** — *"Someone reported a bug in #engineering — find and run the relevant Spur test"*
* **Notion** — *"Run Spur tests for the features listed in this Notion spec"*
* **GitLab** — *"Run tests covering the files in this merge request"*