NewRelic MCP Server

execute_nrql_query

Execute custom NRQL queries to retrieve NewRelic analytics, metrics, and data for exploration and monitoring purposes.

Instructions

Execute an arbitrary NRQL query against NewRelic. Returns raw query results with metadata. Use this for custom analytics, metrics, and data exploration.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "execute_nrql_query"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	The NRQL query to execute. Example: SELECT count(*) FROM Transaction SINCE 1 hour ago
`timeout`	No	Query timeout in seconds (default: 30, max: 120)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 27, 2026

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions 'Returns raw query results with metadata', which adds some behavioral context about output. However, it doesn't disclose critical traits like whether this is a read-only operation (implied but not stated), potential rate limits, authentication requirements, error handling, or data scope limitations. For a tool executing arbitrary queries with no annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with zero waste. The first sentence states the purpose and output, and the second provides usage guidance. It's appropriately sized and front-loaded, with every sentence earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (executing arbitrary queries), no annotations, and no output schema, the description is moderately complete. It covers purpose and usage but lacks behavioral details like safety, limits, or error handling. The schema handles parameters well, but the description doesn't compensate for missing annotations or output schema, leaving gaps for an agent to understand full behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'timeout') thoroughly with examples and constraints. The description adds no additional parameter semantics beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Execute an arbitrary NRQL query against NewRelic' with the verb 'execute' and resource 'NRQL query'. It distinguishes from siblings by specifying it's for 'custom analytics, metrics, and data exploration', though it doesn't explicitly name alternatives. The purpose is specific but could be more differentiated from other query-related tools like 'query_logs' or 'natural_language_to_nrql_query'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context with 'Use this for custom analytics, metrics, and data exploration', which implies when to use it. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., 'query_logs' for logs, 'analyze_golden_metrics' for predefined metrics). The guidance is implied but lacks explicit exclusions or comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.