Sentry

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Prerequisites

• You need a Sentry Auth Token
• Install Python 3.8+
• Create a Meshagent account & authenticate (docs)

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Required Environment Variables

• SENTRY_AUTH_TOKEN: Your Sentry API token

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1. Install Meshagent with Room and tool support

pip install "meshagent[all]"

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2. Sign up and authenticate with Meshagent

meshagent login

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3. Launch the Sentry MCP Agent in a Room

meshagent service test \
    --room=test \
    --role=agent \
    --image=meshagent/mcp-sentry:latest \
    --env SENTRY_AUTH_TOKEN=<YOUR_SENTRY_TOKEN> \
    --env MESHAGENT_PORT=8001 \
    --port="num=8001 path=/webhook liveness=/ type=meshagent.callable" \
    --name=mcp-sentry-service-test

• What this does:
  Launches a Meshagent Room named test, running the Sentry MCP agent as a callable tool.
• Lifecycle note:
  The MCP Sentry server and chatbot are cleaned up automatically when the Room is closed or goes inactive.

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4. Start a Chatbot Connected to the Room

meshagent chatbot join \
    --room=test \
    --agent-name=mcp-sentry \
    --name=mcp-sentry \
    --toolkit=mcp-sentry

• What this does:
  Joins the Room with a chatbot that can use the Sentry MCP toolkit.
• Multiple Toolkits:
  You can use multiple toolkits in the same Room with the same agent.
• Output:
  A link to the Room will be provided for browser access.

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5. Try It!

• Visit the Room link from the command output.
• Start a chat with the agent; use plain English requests (e.g., “Retrieve details for Sentry issue 12345”).
• The chatbot will invoke the MCP Sentry tools running in the Room, retrieving live results from your Sentry account.

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Project Level Deployment (Persistent, Auto-Joint Services)

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1. Create a Persistent Sentry MCP Server Service

meshagent service create \
    --role=agent \
    --image=meshagent/mcp-sentry:latest \
    --env SENTRY_AUTH_TOKEN=<YOUR_SENTRY_TOKEN> \
    --env MESHAGENT_PORT=8001 \
    --port="num=8001 path=/webhook liveness=/ type=meshagent.callable" \
    --name=mcp-sentry-service

• This service is now available in all Rooms in your Meshagent project.

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2. Create a Persistent Sentry Chatbot Service

meshagent service create \
    --image="meshagent/cli:latest" \
    --port="num=9001 path=/agent liveness=/ type=meshagent.callable participant_name=mcp-sentry-chatbot" \
    --env="MESHAGENT_PORT=9001" \
    --name="mcp-sentry-chatbot-service" \
    --command="meshagent chatbot service --agent-name=mcp-sentry-chatbot --toolkit=mcp-sentry"

• Every new Room in your project will auto-join this chatbot, capable of using the Sentry MCP tools.
• You no longer need to run the MCP server or chatbot locally, or set them up each time a Room is launched.

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Tools Available

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Summary

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Tool Details

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Tool: get_sentry_issue

• Investigate production errors and crashes
• Access detailed stacktraces from Sentry
• Analyze error patterns and frequencies
• Get information about when issues first/last occurred
• Review error counts and status

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Learn More

• Meshagent: https://www.meshagent.com/
• Meshagent Documentation: https://docs.meshagent.com
• Meshagent Sentry MCP Tool Image: https://hub.docker.com/r/meshagent/mcp-sentry