IBM Maximo MCP server: assets, work orders, inventory, purchasing, labor, OSLC tools.
Maximo Enterprise MCP MCP server exists for a simple reason — assistants are far more useful when they can act on Maximo Enterprise MCP directly instead of describing what you should do. IBM Maximo MCP server: assets, work orders, inventory, purchasing, labor, OSLC tools.
A production-focused integration that brings IBM Maximo Asset Management into AI workflows through the Model Context Protocol.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
Once Maximo Enterprise MCP is connected, these are the calls the assistant has available:
Cursor — Add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (per project):OpenAI — Use GET /v1/providers/openai-tools to retrieve OpenAI-compatible tool definitions, then execute the selected tool via POST /v1/tools/{tool_name}Gemini — Use GET /v1/providers/gemini-tools to retrieve Gemini function declarations from the same shared tool registryGrok — Use GET /v1/providers/grok-tools. The payload is OpenAI-compatible so the same orchestration pattern worksBuild — The Build tool exposed by this serverAzure — The Azure tool exposed by this serverYou will need 8 environment variables: MAXIMO_URL, MAXIMO_HOST, AUTH_MODE, MAXIMO_USERNAME, MAXIMO_PASSWORD, CURRENT_USER_ROLE, MCP_ACCESS_TOKEN, REDIS_URL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Maximo Enterprise MCP's toolset — Cursor, OpenAI, Gemini and 3 more — is a fair guide to whether it matches your workflow. It is maintained by MaxisTechnology-Dev; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Maximo Enterprise MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Cursor | Add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (per project): |
| OpenAI | Use GET /v1/providers/openai-tools to retrieve OpenAI-compatible tool definitions, then execute the selected tool via POST /v1/tools/{tool_name}. |
| Gemini | Use GET /v1/providers/gemini-tools to retrieve Gemini function declarations from the same shared tool registry. |
| Grok | Use GET /v1/providers/grok-tools. The payload is OpenAI-compatible so the same orchestration pattern works. |
| Build | The Build tool exposed by this server. |
| Azure | The Azure tool exposed by this server. |
{
"mcpServers": {
"maximo": {
"command": "uvx",
"args": ["maximo-enterprise-mcp"],
"env": {
"MAXIMO_URL": "https://your-maximo-host.com/maximo/oslc",
"MAXIMO_HOST": "https://your-maximo-host.com",
"AUTH_MODE": "basic",
"MAXIMO_USERNAME": "your-username",
"MAXIMO_PASSWORD": "your-password",
"CURRENT_USER_ROLE": "readonly"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| MAXIMO_URL | Endpoint or connection string the server talks to. | Yes |
| MAXIMO_HOST | Endpoint or connection string the server talks to. | Optional |
| AUTH_MODE | Configuration value read at startup. | Optional |
| MAXIMO_USERNAME | Configuration value read at startup. | Optional |
| MAXIMO_PASSWORD | Configuration value read at startup. | Optional |
| CURRENT_USER_ROLE | Configuration value read at startup. | Optional |
| MCP_ACCESS_TOKEN | Credential the server authenticates with. | Yes |
| REDIS_URL | Endpoint or connection string the server talks to. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.