NewRelic MCP Server

list_recent_issues

Retrieve AI-detected incidents from NewRelic with filtering options for states, priorities, and entities to monitor system health and investigate problems.

Instructions

List recent AI-detected issues from NewRelic. Uses the experimental aiIssues API to fetch active and recent incidents with priority and state information.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_recent_issues"
  }
}

Input Schema

Name	Required	Description
`states`	No	Filter by issue states (default: ACTIVATED, CREATED)
`priorities`	No	Filter by issue priorities
`entityGuids`	No	Filter by entity GUIDs
`limit`	No	Maximum number of issues to return (default: 50, max: 200)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 27, 2026

TDQS

B3.4/5.0

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 of behavioral disclosure. It mentions the API is 'experimental' and that it fetches 'active and recent incidents', but does not disclose critical traits like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the return format looks like (e.g., pagination, structure). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 sentences, front-loaded with the core purpose and method, and includes no wasted words. Every phrase ('List recent AI-detected issues', 'Uses the experimental aiIssues API', 'fetch active and recent incidents with priority and state information') directly contributes to understanding the tool's function and scope.

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

Completeness2/5

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

Given the complexity of a tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., read-only status, rate limits), does not explain the return values or format, and provides minimal guidance on usage versus siblings. For a tool interacting with an experimental API and filtering issues, more context is needed to ensure the agent can use it effectively.

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%, with each parameter well-documented in the schema (e.g., 'states' with enum values, 'limit' with default and max). The description adds no additional parameter semantics beyond what the schema provides, such as explaining how filters interact or providing examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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

Purpose5/5

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

The description clearly states the specific action ('List recent AI-detected issues'), the resource source ('from NewRelic'), and the method ('Uses the experimental aiIssues API'). It distinguishes itself from siblings like 'list_alert_conditions' or 'search_incident' by focusing on AI-detected issues with priority and state information, not general alerts or incidents.

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 implies usage for fetching active and recent incidents with priority and state, but does not explicitly state when to use this tool versus alternatives like 'list_alert_conditions' or 'search_incident'. It provides context ('experimental aiIssues API') but lacks clear exclusions or named alternatives, leaving the agent to infer based on the tool's focus.

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