About AIOps Field Notes
AIOps Field Notes is an MCP server for operators working with AI infrastructure and network configurations. Its tools estimate model costs, check GPU memory fit, review MCP configurations, and analyze network configuration changes and compliance.
Key features
- 01Estimate monthly LLM costs
- 02Check model fit in GPU VRAM
- 03Audit MCP configuration risks
- 04Look up MCP server details
- 05Explain network configuration diffs
- 06Check CIS/PCI configuration basics
Use cases
- 01Compare model costs before setting a budget
- 02Check whether a model fits a GPU before ordering
- 03Review MCP server access and configuration risks
- 04Assess network changes or CIS/PCI basics
MCP support
Exposes operational tools to MCP clients, including MCP configuration audits and server lookups. The audit accepts configuration text and does not read local files.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
docker run -i --rm ghcr.io/labaccessnow/aiops-field-notes-mcp:0.1.1Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
ghcr.io/labaccessnow/aiops-field-notes-mcp:0.1.1stdioTools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAIOps Field Notes MCP server
Four things I kept doing by hand while running AI in production, turned into tools an agent can call: price a model before committing a budget, size a GPU before ordering one, review an MCP config before trusting it, and explain what changed in a network device config.
Free, MIT, no account, no telemetry.
Install
Pin the version — see audit_mcp_config for why.
Claude Code
claude mcp add aiops-field-notes -- npx -y github:labaccessnow/aiops-field-notes-mcp#v0.1.1
Claude Desktop, Cursor, or any client with a JSON config
{
"mcpServers": {
"aiops-field-notes": {
"command": "npx",
"args": ["-y", "github:labaccessnow/aiops-field-notes-mcp#v0.1.1"]
}
}
}
Docker
{
"mcpServers": {
"aiops-field-notes": {
"command": "docker",
"args": ["run", "-i", "--rm", "ghcr.io/labaccessnow/aiops-field-notes-mcp:0.1.1"]
}
}
}
Node 18 or newer for the npx route; nothing else to configure either way. The server is also in the
official MCP registry as io.github.labaccessnow/aiops-field-notes-mcp.
Tools
| Tool | What it answers |
|---|---|
estimate_llm_cost | What does this workload cost per month on each model, cheapest first? |
check_vram_fit | Does a 70B model at Q4 fit on my 4090 with a 32K context? |
audit_mcp_config | Is this MCP config safe to trust — what can each server actually reach? |
lookup_mcp_server | What is this server, who maintains it, and is it reference, vendor or community? |
explain_config_diff | What changed on this router between two snapshots, and how bad is it? |
check_config_compliance | Does this device config pass the CIS/PCI basics? |
latest_field_note | What happened in AI ops this week? |
audit_mcp_config
The one I use most. Paste a client config and it reads it the way a reviewer would — every server entry is an access grant, so the questions are what it can reach, who publishes it, and what happens the next time it auto-updates:
6 servers reviewed — 7 high, 6 medium, 0 low.
files
[high] Package version is not pinned
files runs "@modelcontextprotocol/server-filesystem" at whatever version is published when it launches.
fix: Pin it — "@modelcontextprotocol/server-filesystem@<version>" for npm, "@modelcontextprotocol/server-filesystem==<version>" for uvx — and bump deliberately.
[high] Filesystem root is wide open
files is granted "/" — that is the whole home directory or the whole disk, including keys and browser profiles.
fix: Scope the server to the project directories the agent actually works in.
[medium] Installs without prompting
files passes -y, so a new release installs and runs with no confirmation.
fix: Keep the flag only alongside a pinned version, so what installs is what you reviewed.
notes
[high] Package version is not pinned
notes runs "some-random-notes-mcp" at whatever version is published when it launches.
fix: Pin it — "some-random-notes-mcp@<version>" for npm, "some-random-notes-mcp==<version>" for uvx — and bump deliberately.
[high] Secret is stored in the config file
notes has NOTES_API_KEY written out in plaintext; config files get committed, synced and screenshared.
fix: Reference the environment instead — "NOTES_API_KEY": "${NOTES_API_KEY}" — and keep the value in your secret store.
[medium] Installs without prompting
notes passes -y, so a new release installs and runs with no confirmation.
fix: Keep the flag only alongside a pinned version, so what installs is what you reviewed.
[medium] Publisher not in the tracked set
"some-random-notes-mcp" is not one of the reference or vendor servers this directory tracks.
fix: Read the source and check the publisher before wiring it into an agent that touches production.
It also flags shell entrypoints, privileged containers, host-root mounts, credentials on the command line, database access that is not read-only, unauthenticated remote servers, and anything reached over plain HTTP.
What it does not do
- No account, no signup, no key.
- No telemetry. Nothing about your usage leaves your machine.
- No filesystem access.
audit_mcp_configtakes the config as text, so the server never reads your disk. - One network call in the whole package:
latest_field_notefetches a public RSS feed. Skip that tool and this server never opens a socket.
The cost table, GPU list and server directory match the calculators at aiopsfieldnotes.com/tools and get re-verified with each weekly episode. Pricing moves — check the vendor's page before you commit a budget to it.
Where the network tools come from
explain_config_diff and check_config_compliance are the read-only core of
DriftWatch, which runs the same rules nightly across a fleet and
keeps the history. Here you get the single-shot version, on two snapshots you paste, with no storage and
no scheduler. That is deliberate: the rules are the useful part, and they work fine on their own.
Licence
MIT. Written by James Son — network, security, and automation engineer — and tested in a live multi-vendor lab. Contributions and corrections welcome.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.