New Relic MCP Server

A Model Context Protocol (MCP) server that provides programmatic access to New Relic APIs, enabling AI assistants and other tools to interact with New Relic monitoring and observability data.

Features

APM Application Management: List and retrieve application details, metrics, and metric data
NRQL Queries: Execute NRQL queries via NerdGraph
Log Parsing Rules: Intelligently create, manage, and test GROK patterns for log parsing
Alert Policies: List and manage alert policies
Synthetic Monitoring: Access synthetic monitor information
Dashboards: List and retrieve dashboard configurations
Entity Search: Search across all New Relic entities
Infrastructure: Monitor servers and infrastructure components
Deployments: Track and create application deployments
User Management: List and manage users
NerdGraph: Execute custom GraphQL queries

Installation

Option 1: Install from PyPI (Recommended)

pip install newrelic-mcp-server

Option 2: Install from Source

# Clone this repository
git clone https://github.com/piekstra/newrelic-mcp-server.git
cd newrelic-mcp-server

# Install in development mode
pip install -e .

Configuration

Secure Credential Setup (Recommended for macOS)

For enhanced security on macOS, use the built-in keychain storage instead of environment variables:

# Run the secure setup wizard
newrelic-mcp-setup

# Or set up manually with Python
python -m newrelic_mcp.credentials

This will securely store your API key and account ID in the macOS Keychain, eliminating the need to store sensitive credentials in plain text files.

Environment Variables (Alternative)

If you prefer environment variables or are not on macOS, you can use:

# Required
export NEWRELIC_API_KEY="your-api-key-here"  # Your New Relic User API key

# Optional
export NEWRELIC_REGION="US"  # or "EU" (default: "US")
export NEWRELIC_ACCOUNT_ID="your-account-id"  # Required for some operations

Note: The server will automatically prefer keychain storage over environment variables when both are available.

Getting Your API Key

Log in to New Relic
Navigate to the API Keys page
Create a new User API key (starts with NRAK)
Copy the key and set it as the NEWRELIC_API_KEY environment variable

Usage

With Claude Desktop

Option 1: Using Secure Keychain (Recommended for macOS)

After setting up credentials with newrelic-mcp-setup, add the following to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "newrelic": {
      "command": "newrelic-mcp-server",
      "env": {
        "NEWRELIC_REGION": "US"
      }
    }
  }
}

Option 2: Using Environment Variables (Less Secure)

{
  "mcpServers": {
    "newrelic": {
      "command": "newrelic-mcp-server",
      "env": {
        "NEWRELIC_API_KEY": "your-api-key-here",
        "NEWRELIC_REGION": "US",
        "NEWRELIC_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

Security Note: Option 1 is strongly recommended as it keeps your sensitive API key out of configuration files.

With Other MCP Clients

# Start the server directly
newrelic-mcp-server

# Or run as a module
python -m newrelic_mcp

Available Tools

Application Management

list_applications - List all APM applications
get_application - Get details for a specific application
get_application_metrics - Get available metrics for an application
get_application_metric_data - Get metric data with time range filtering

Querying

query_nrql - Execute NRQL queries for data analysis
nerdgraph_query - Execute custom NerdGraph GraphQL queries

Monitoring

list_alert_policies - List all alert policies
get_alert_policy - Get specific alert policy details
list_synthetic_monitors - List synthetic monitors
get_synthetic_monitor - Get synthetic monitor details

Dashboards & Visualization

list_dashboards - List all dashboards
get_dashboard - Get dashboard configuration and widgets

Infrastructure

list_servers - List monitored servers
get_server - Get server details
search_entities - Search across all entity types

Deployment Tracking

list_deployments - List application deployments
create_deployment - Record new deployments

User Management

list_users - List account users
get_user - Get user details

Log Parsing Rules

list_log_parsing_rules - List all log parsing rules for an account
create_log_parsing_rule - Create a new log parsing rule with GROK pattern and NRQL filter
update_log_parsing_rule - Update an existing log parsing rule
delete_log_parsing_rule - Delete a log parsing rule
test_log_parsing_rule - Test a GROK pattern against sample logs (generates pattern if not provided)
generate_log_parsing_rule - Intelligently generate GROK patterns from log samples or NRQL queries

Credential Management

manage_credentials - Securely manage API credentials in keychain
- action="status" - Show current credential storage status
- action="store" - Store new credentials securely (requires api_key parameter)
- action="delete" - Remove all stored credentials

Examples

Query Application Performance

# List all applications
await list_applications()

# Get specific application metrics
await get_application_metric_data(
    app_id='123456',
    metric_names=['HttpDispatcher', 'Apdex'],
    from_time='2024-01-01T00:00:00Z',
    to_time='2024-01-02T00:00:00Z'
)

Execute NRQL Query

await query_nrql(
    account_id='1234567',
    nrql='SELECT average(duration) FROM Transaction WHERE appName = "My App" SINCE 1 hour ago'
)

Search Entities

await search_entities(
    query='name LIKE "%production%"',
    limit=50
)

Create Deployment Marker

await create_deployment(
    app_id='123456',
    revision='v2.0.1',
    description='Production deployment',
    user='deploy-bot',
    changelog='Fixed critical bug in payment processing'
)

Log Parsing Rules

Generate GROK Pattern from Log Samples

# Generate parsing rule from sample logs (sanitized example)
await generate_log_parsing_rule(
    log_samples=[
        "Updated info for user 12345678-1234-5678-9012-123456789012 (updated 1 rows, expected 1) - updated values: EmailAddress test@example.com, FirstName TestName, LastName TestLast, Active True, EmailConfirmed False"
    ]
)

# Returns generated GROK pattern and NRQL filter:
# {
#   "grok_pattern": "Updated info for user %{UUID:user_id:string} \\(updated %{INT:rows_affected:long} rows, expected %{INT:rows_expected:long}\\) - updated values: EmailAddress %{DATA:email:string}, FirstName %{WORD:first_name:string}, LastName %{WORD:last_name:string}, Active %{WORD:active:string}, EmailConfirmed %{WORD:emailconfirmed:string}",
#   "nrql_pattern": "SELECT * FROM Log WHERE message LIKE 'Updated info for user % (updated % rows, expected %) - updated values: EmailAddress %, FirstName %, LastName %, Active %, EmailConfirmed %'",
#   "suggested_description": "Auto-generated parsing rule for single log sample"
# }

Create Log Parsing Rule

# Create a new parsing rule using generated patterns
await create_log_parsing_rule(
    description="User update audit log parsing",
    grok="Updated info for user %{UUID:user_id:string} \\(updated %{INT:rows_affected:long} rows, expected %{INT:rows_expected:long}\\) - updated values: EmailAddress %{DATA:email:string}, FirstName %{WORD:first_name:string}, LastName %{WORD:last_name:string}, Active %{WORD:active:string}, EmailConfirmed %{WORD:emailconfirmed:string}",
    nrql="SELECT * FROM Log WHERE message LIKE 'Updated info for user % (updated % rows, expected %) - updated values: EmailAddress %, FirstName %, LastName %, Active %, EmailConfirmed %'",
    enabled=True
)

Generate from Live Logs

# Generate patterns from logs matching a query
await generate_log_parsing_rule(
    log_query="service = 'user-service' AND message LIKE '%Updated info for user%'",
    time_range="1 day ago",
    field_hints={"user_id": "UUID", "email": "EMAIL"}
)

Test Parsing Rules

# Test a GROK pattern against samples
await test_log_parsing_rule(
    log_samples=["Updated info for user 12345678-1234-5678-9012-123456789012 (updated 1 rows, expected 1)"],
    grok_pattern="Updated info for user %{UUID:user_id:string} \\(updated %{INT:rows_affected:long} rows, expected %{INT:rows_expected:long}\\)"
)

Manage Parsing Rules

# List all parsing rules
await list_log_parsing_rules()

# Update a rule
await update_log_parsing_rule(
    rule_id="12345",
    description="Updated user audit log parsing",
    enabled=False
)

# Delete a rule
await delete_log_parsing_rule(rule_id="12345")

Development

# Clone the repository
git clone https://github.com/piekstra/newrelic-mcp-server.git
cd newrelic-mcp-server

# Install in development mode
pip install -e .[dev]

# Install pre-commit hooks
pre-commit install

# Run the server
newrelic-mcp-server

# Run tests (when available)
pytest

# Format code
black newrelic_mcp

# Lint code
flake8 newrelic_mcp

# Run all pre-commit checks
pre-commit run --all-files

Dependencies

fastmcp - FastMCP framework for building MCP servers
httpx - Async HTTP client for API requests
python-dotenv - Environment variable management (optional)

API Rate Limits

Be aware of New Relic's API rate limits:

REST API v2: Subject to rate limiting per account
NerdGraph: Higher rate limits but still enforced
Synthetic Monitoring API: 3 requests per second

Security

Use keychain storage on macOS: Run newrelic-mcp-setup to store credentials securely in the system keychain
Never commit API keys to version control
Avoid storing sensitive credentials in plain text configuration files
API keys should have minimal required permissions
Consider using separate keys for different environments
The server automatically prioritizes keychain storage over environment variables for enhanced security

Troubleshooting

Authentication Errors

Ensure your API key starts with NRAK
Verify the key has the necessary permissions
Check if you're using the correct region (US/EU)
If using keychain storage, verify credentials are stored with manage_credentials action="status"
Try re-running newrelic-mcp-setup if keychain access fails

Rate Limiting

If you encounter rate limit errors:

Implement exponential backoff in your client code
Cache frequently accessed data
Batch operations where possible

Connection Issues

Verify network connectivity
Check firewall rules for API endpoints
Ensure correct base URLs for your region

Python Environment

Ensure Python 3.10+ is installed
Install dependencies with pip install -r requirements.txt
Check that the script is executable: chmod +x newrelic_mcp_server.py

Command Not Found Issues

If you encounter "command not found" errors after installation:

Try using the full path to the installed package:
- Linux/macOS (user install): ~/.local/bin/newrelic-mcp-server
- macOS (Python framework): /Library/Frameworks/Python.framework/Versions/3.13/bin/newrelic-mcp-server
- System-wide: /usr/local/bin/newrelic-mcp-server
Or add the installation directory to your PATH: export PATH="$HOME/.local/bin:$PATH"
In Claude Desktop config, use the full path if the command isn't found:
{ "mcpServers": { "newrelic": { "command": "/Library/Frameworks/Python.framework/Versions/3.13/bin/newrelic-mcp-server", "env": { "NEWRELIC_API_KEY": "your-api-key-here", "NEWRELIC_REGION": "US", "NEWRELIC_ACCOUNT_ID": "your-account-id" } } } }

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT

Support

For issues and questions:

GitHub Issues: Create an issue
New Relic Documentation: docs.newrelic.com

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables AI assistants to interact with New Relic monitoring and observability data through programmatic access to New Relic APIs. Supports APM management, NRQL queries, alert policies, synthetic monitoring, dashboards, infrastructure monitoring, and deployment tracking.

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