How to Use the New Relic AI (LLM Observability) MCP in VS Code Copilot

Q: How do we share New Relic AI (LLM Observability) configurations in VS Code Copilot?

You create a .vscode/mcp.json file in your repository root and commit it. This allows your entire engineering team to access the MCP Server automatically using their local Copilot agents.

Q: Can VS Code Copilot run custom dashboards using New Relic AI (LLM Observability)?

No, but it can find them. Your agent uses the listdashboards tool to locate your active visualization screens, helping you keep track of where your team monitors live LLM performance.

Q: How do I check alert statuses with New Relic AI (LLM Observability) in VS Code Copilot?

You use the listalertpolicies tool to inspect active alert rules. This lets your team verify that your monitoring setup will catch production anomalies before they impact customers.

Q: Can I track individual model latency?

Yes. The queryllmlatency tool fetches latency metrics for your models. Your agent can then analyze this data to suggest optimizations for slower endpoints.

Keep your team aligned on LLM performance with New Relic telemetry inside VS Code Copilot.

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Connect New Relic AI (LLM Observability) MCP to VS Code Copilot

Create your Vinkius account to connect New Relic AI (LLM Observability) to VS Code Copilot and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup New Relic AI (LLM Observability) with VS Code Copilot

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Shared Telemetry Queries inside VS Code Copilot

The `custom_nrql` tool gives your development team direct, read-only access to New Relic metrics inside the editor. By committing the MCP Server configuration to your repository, every developer can run queries without leaving their workspace. This makes it easy to verify how code changes affect production. Developers run queries to compare performance metrics before and after pulling updates.

Monitor Costs and User Feedback in Chat

The `query_llm_costs` tool pulls live token cost data directly into your team chat. This keeps your budget transparent and helps developers spot expensive prompt loops early in the cycle. You can also call the MCP tool `query_llm_feedback` to check qualitative user ratings. It lets your team quickly identify which prompts are causing negative user experiences in production.

Track Errors and Dispatch Events via MCP Server

The `query_llm_errors` tool identifies active error trends across your deployments. Your agent reads these error logs to help you quickly diagnose connection drops or model rate limits. Use `post_custom_event` to send custom telemetry directly from your terminal or chat sessions. This lets you log test scenarios and verify that your production monitoring captures the right data.

Setup guide

Set up New Relic AI (LLM Observability) MCP in VS Code Copilot

Prerequisites

VS Code 1.99 or later with GitHub Copilot extension
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and run "MCP: Add Server". Select HTTP (Streamable) as the server type. VS Code will create .vscode/mcp.json in your workspace.
2

Add the New Relic AI (LLM Observability) MCP

Paste the JSON snippet shown on the right into your .vscode/mcp.json. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Switch to Agent mode

Open Copilot Chat (Cmd+Shift+I / Ctrl+Shift+I) and switch to Agent mode using the dropdown. MCP tools are only available in Agent mode — they do not appear in Edit or Ask modes.
4

Verify the connection

In the Copilot Chat input, type # to list available tools. You should see the New Relic AI (LLM Observability) tools listed. Try asking: "List my recent New Relic AI (LLM Observability) transactions" and Copilot will invoke them automatically.

.vscode/mcp.json

{
  "mcpServers": {
    "new-relic-ai-llm-observability-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about New Relic AI (LLM Observability) MCP in VS Code Copilot

You create a `.vscode/mcp.json` file in your repository root and commit it. This allows your entire engineering team to access the MCP Server automatically using their local Copilot agents.

No, but it can find them. Your agent uses the `list_dashboards` tool to locate your active visualization screens, helping you keep track of where your team monitors live LLM performance.

You use the `list_alert_policies` tool to inspect active alert rules. This lets your team verify that your monitoring setup will catch production anomalies before they impact customers.

Yes. The `query_llm_latency` tool fetches latency metrics for your models. Your agent can then analyze this data to suggest optimizations for slower endpoints.

This MCP Server handles read-only queries for metrics like token costs, latency, and error logs. Your New Relic API keys are securely managed within VS Code's user configuration, preventing exposure in shared repositories.

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