ServiceNow Incident MCP Server

ServiceNow Incident MCP Server

A production-ready MCP server that turns any MCP-compatible AI assistant into an AI-powered ServiceNow Incident Management Assistant, exposing incidents, users, CMDB records, and knowledge articles through validated tools.

Category
Visit Server

README

ServiceNow Incident MCP Server

A production-ready Model Context Protocol (MCP) server that turns any MCP-compatible AI assistant (Claude, Cursor, etc.) into an AI-powered ServiceNow Incident Management Assistant. It exposes ServiceNow incidents, users, CMDB records, and knowledge articles through clean, validated MCP tools.

AI Assistant  ──►  MCP Server (Node.js + TypeScript)  ──►  ServiceNow REST API  ──►  ServiceNow Instance

Features

  • 12 MCP tools across three phases (read, actions, AI-powered helpers).
  • Reusable ServiceNow REST client (Axios) with Basic auth, request timeout, and exponential-backoff retry on transient failures.
  • Zod-validated inputs for every tool.
  • Structured logging to stderr with automatic secret redaction — passwords and auth headers are never logged.
  • Self-contained, deterministic AI tools — no external LLM or API key required.
  • Modular, enterprise-grade architecture with a full unit-test suite (Vitest).

Project Structure

src/
├── clients/
│   └── servicenowClient.ts   # Axios wrapper: GET/POST/PATCH, auth, retry, timeout, Table API helpers
├── config/
│   └── env.ts                # Zod-validated environment configuration
├── schemas/
│   └── index.ts              # Zod input schemas for every tool
├── services/
│   ├── incidentService.ts    # incident reads/writes + journal (comments/work notes)
│   ├── userService.ts        # sys_user lookups
│   ├── cmdbService.ts        # cmdb_ci search
│   ├── knowledgeService.ts   # kb_knowledge search
│   ├── aiService.ts          # rule-based classification, routing, summaries
│   └── index.ts              # service container
├── tools/
│   ├── phase1.read.ts        # get_incident, search_incidents, get_user, search_cmdb, get_knowledge_article
│   ├── phase2.actions.ts     # create_incident, update_incident, assign_incident, close_incident
│   ├── phase3.ai.ts          # classify_incident, incident_summary, suggest_assignment_group
│   ├── helpers.ts            # response builders + central error handling
│   └── index.ts              # registers all tools
├── types/
│   └── servicenow.ts         # shared TypeScript interfaces
├── utils/
│   ├── logger.ts             # structured logger + redaction
│   ├── errors.ts             # ServiceNowError + safe message mapping
│   └── format.ts             # raw ServiceNow record → clean object mappers
├── server.ts                 # builds the MCP server (config → client → services → tools)
└── index.ts                  # entrypoint (stdio transport)
tests/                        # Vitest unit tests

Setup

Prerequisites

  • Node.js 18+
  • A ServiceNow instance and an account with Table API access.

Install & build

npm install
cp .env.example .env      # then edit .env with your instance + credentials
npm run build

Configure environment

Variable Required Default Description
SNOW_INSTANCE yes Instance base URL, e.g. https://dev123.service-now.com
SNOW_USERNAME yes Basic auth username
SNOW_PASSWORD yes Basic auth password
SNOW_TIMEOUT_MS no 30000 Per-request timeout (ms)
SNOW_MAX_RETRIES no 3 Retry attempts on network/429/5xx errors
SNOW_DEFAULT_LIMIT no 10 Default result count for search tools
LOG_LEVEL no info debug | info | warn | error

Run

npm start          # run the built server (dist/index.js)
npm run dev        # run from TypeScript with hot reload

The server speaks MCP over stdio.

Tool Reference

Phase 1 — Read

Tool Input Returns
get_incident { incidentNumber } Number, state, priority, assignment group, assignee, descriptions, timestamps
search_incidents { assignedTo?, caller?, priority?, state?, limit? } Matching incidents (newest first)
get_user { userName } User name, email, phone, title, department
search_cmdb { name, limit? } Matching CMDB CIs
get_knowledge_article { keyword, limit? } Matching published KB articles

Phase 2 — Actions

Tool Input Effect
create_incident { shortDescription, description?, callerId?, assignmentGroup?, impact?, urgency? } Creates an incident → { incidentNumber, sysId }
update_incident { incidentNumber, workNote?, comment? } Appends work note / comment
assign_incident { incidentNumber, assignmentGroup?, assignedTo? } Updates assignment
close_incident { incidentNumber, closeNotes, closeCode? } Resolves & closes the incident

Phase 3 — AI-powered (deterministic, rule-based)

Tool Input Returns
classify_incident { issue } { impact, urgency, priority, reason }
incident_summary { incidentNumber } Executive summary built from the incident + journal
suggest_assignment_group { issue } { assignmentGroup, reason }

Classification Logic

classify_incident mirrors ServiceNow's Impact × Urgency → Priority matrix. It infers impact from the scope of the issue and urgency from time-critical language, both via documented keyword rules (case-insensitive, first match wins).

Scope detected in the issue text Impact Urgency Priority
Entire office / company / site / everyone 1 1 P1
Entire team / department / group / floor 2 2 P2
Single employee / one user / "my…" 3 2 P3
Minor / cosmetic / typo / question / slow 3 3 P4
Unrecognized (default) 3 3 P3

Time-critical words (urgent, critical, outage, down, blocked, cannot work, production, emergency, …) escalate urgency → 1, and priority is recomputed from the matrix:

 Impact \ Urgency   1    2    3
        1          P1   P2   P3
        2          P2   P3   P4
        3          P3   P4   P4

suggest_assignment_group uses a keyword→group routing table (VPN/network → Network Operations, email/Outlook → Messaging & Collaboration, password/login → Identity & Access Management, database/SQL → Database Team, laptop/printer → Desktop Support, phone/call forwarding → Telephony, application/crash → Application Support), defaulting to Service Desk.

These rules live in src/services/aiService.ts and are easy to tune for your org's groups.

Sample MCP Client Configuration

Add the server to your MCP client config. Use absolute paths and supply credentials via env.

Claude Desktop (claude_desktop_config.json) / Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "servicenow": {
      "command": "node",
      "args": ["/absolute/path/to/servicenow-mcp/dist/index.js"],
      "env": {
        "SNOW_INSTANCE": "https://dev12345.service-now.com",
        "SNOW_USERNAME": "admin",
        "SNOW_PASSWORD": "your-password",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Run npm run build first so dist/index.js exists.

Testing & Quality

npm test            # run the unit test suite (Vitest)
npm run test:coverage
npm run typecheck   # tsc --noEmit
npm run lint        # eslint

Tests are fully offline — the ServiceNow client is mocked, and the AI tools are deterministic.

Manual smoke test with MCP Inspector

npm run build
npx @modelcontextprotocol/inspector node dist/index.js

Then list tools and try classify_incident / suggest_assignment_group (work offline) or a read tool against your instance.

Security

  • Credentials are read only from environment variables; .env is git-ignored.
  • The logger redacts any password / authorization / token / secret field, and logs go to stderr only (keeping the MCP stdout channel clean).
  • All HTTP requests use a configurable timeout and retry transient failures with exponential backoff; 4xx errors (other than 429) fail fast with safe messages.

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured