MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
LLM Eval Harness is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
2
tools declared
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trust score
local
runs on your machine
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calls last 7d
What it does
Run a golden set and gate LLM model swaps on accuracy, with latency and cost per request.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user llm-eval-harness-mcp '--' docker run -i --rm ghcr.io/razilzul/llm-eval-harness:1.0.0
# 2 — ask your agent for something these tools do
# run_golden_set, compare_models
LLM Eval Harness costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
RAAll 1 server
razilzul1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts LLM Eval Harness as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. It has not been scanned here, so read the surface, and its source, before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Docker installed and running. Only the required variables are passed into the container. To set an optional one (ANTHROPIC_API_KEY, GEMINI_API_KEY), add -e NAME before the image name and give it a value in the entry’s env: an unset -e would clear the default the image sets. Claude Code registers it in one command; --scope user makes it available in every project. The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user llm-eval-harness-mcp '--' docker run -i --rm ghcr.io/razilzul/llm-eval-harness:1.0.0Reconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own image: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
Nothing is required, but it can take ANTHROPIC_API_KEY (Only needed to evaluate anthropic:* models. Not needed for mock models.), GEMINI_API_KEY (Only needed to evaluate gemini:* models. Not needed for mock models.) if you want to set them.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
2 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Read from its source code. A server that builds its tool list when it starts can list more tools than this.
Run one model over the golden set. Returns accuracy overall and on trap cases, mean latency and cost per request, and the failing cases.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | yes | — | |
| judge | no | — |
A/B two models on the golden set and return the gate verdict. The candidate must match or beat the baseline on accuracy, overall and on traps; only then do latency and cost matter.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| baseline | yes | — | |
| candidate | yes | — | |
| judge | no | — |
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install LLM Eval Harness or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
—Grade not published
No rule-by-rule report on file for this release
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
This listing ships as a container image. The scanner reads packages on npm and PyPI and public source; an image is neither, so there is no grade here — not a withheld one, an unmeasured one.
Release history
It installs from a container image rather than from a versioned package, so the command cannot be pinned to a release. The history below is for reading; the current release on file is 1.0.0.
1.0.0
undated
latestNo release note was published with this version.
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no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish LLM Eval Harness can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.