MCP.Market

Metrillm MCP server

by MetriLLM·io.github.MetriLLM/metrillm·v0.2.6·8 stars
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Benchmark local LLM models — speed, quality & hardware fitness verdict from any MCP client

B80/100grade B
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Safety scan
B80/100

full report

Adoption
Growing

8 stars59 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

Metrillm tools (4, 2 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • get_results

    Retrieve previously saved benchmark results from ~/.metrillm/results/. Optionally filter by model name. Returns an array of full benchmark result objects.

  • list_models

    List all LLM models available locally on the inference runtime (e.g. Ollama). Returns model name, size, parameter count, quantization, and family.

  • run_benchmarkwrite action

    Run an MetriLLM benchmark on a local model. Measures performance (tokens/s, TTFT, memory) and optionally quality (reasoning, math, coding, instruction following, structured output, multilingual). Returns a detailed fitness verdict. Warning: benchmarks take 30s to 5+ minutes depending on model size.

  • share_resultwrite action

    Upload a benchmark result to the public MetriLLM leaderboard. Uses official upload defaults; METRILLM_* environment variables can override for self-hosted deployments. The resultFile must be an absolute path to a JSON file in ~/.metrillm/results/.

Public scan report

scanner v0.1.10 · 2026-09-27 · same rubric, same numbers if you re-run it

no findings
  • Code scan2 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 117 days ago8/15
  • Maintainer identityregistry namespace matches repository owner7/10
Overall 80/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Metrillm compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
80/100
higher than 60% of 4,460 AI and agents servers
Tools
4
the typical AI and agents server has 6
Tools that change things
50%
typical: 0%. Fewer write tools means less an agent can break
GitHub stars
8
more than 82% of 2,711 AI and agents servers
Downloads a week
59
more than 49% of 1,538 AI and agents servers with a package

What its tools let an agent do: 2 read · 1 send or post · 1 run or deploy

Install Metrillm in Claude Code, Cursor or VS Code

Runs npx -y metrillm-mcp on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add metrillm -- npx -y metrillm-mcp
Add to Cursor

What the publisher says

From the Metrillm repository's README, as published. We do not edit it. Read it on GitHub

MetriLLM

Benchmark your local LLM models in one command. Speed, quality, hardware fitness — with a shareable score and public leaderboard.

Think Geekbench, but for local LLMs on your actual hardware.

npm install -g metrillm@latest
metrillm bench

What You Get

  • Performance metrics: tokens/sec, time to first token, memory usage, load time
  • Quality evaluation: reasoning, coding, math, instruction following, structured output, multilingual (14 prompts, 6 categories)
  • Global score (0-100): 30% hardware fit + 70% quality
  • Verdict: EXCELLENT / GOOD / MARGINAL / NOT RECOMMENDED
  • One-click share: --share uploads your result and gives you a public URL + leaderboard rank

Real Benchmark Results

From the public leaderboard — all results below were submitted with metrillm bench --share.

Key takeaway: Small models (1-4B) fly on Apple Silicon. Larger models (14B+) with thinking chains can choke even on capable hardware. See full leaderboard →

Install

Requires Node 20+ and a local runtime:

Ollama or LM Studio.

# Install globally
npm install -g metrillm@latest
metrillm bench

# Alternative package managers
pnpm add -g metrillm@latest
bun add -g metrillm@latest

# Homebrew
brew install MetriLLM/metrillm/metrillm

# Or run without installing
npx metrillm@latest bench

Usage

# Interactive mode — pick models from a menu
metrillm bench

# Benchmark a specific model
metrillm bench --model gemma3:4b

# Benchmark with LM Studio backend
metrillm bench --backend lm-studio --model qwen3-8b

# Benchmark all installed models
metrillm bench --all

# Share your result (upload + public URL + leaderboard rank)
metrillm bench --share

# CI/non-interactive mode
metrillm bench --ci-no-menu --share

# Force unload after each model (useful for memory isolation)
metrillm bench --all --unload-after-bench

# Export results locally
metrillm bench --export json
metrillm bench --export csv

Upload Configuration (CLI + MCP)

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Metrillm: common questions

Is Metrillm MCP server safe?
Mostly: it is graded B (80/100). Read the Metrillm safety report
How do I install Metrillm?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Metrillm need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Metrillm maintained?
The last commit was 155 days ago (2026-06-02). The latest release is v0.2.6.
What can I use instead of Metrillm?
Servers from other publishers that do the same job: Image MCP server and Claude Prompts MCP server.
How does Metrillm compare with other AI and agents MCP servers?
safety score 80/100 (higher than 60% of 4,460 AI and agents servers); tools 4 (the typical AI and agents server has 6); tools that change things 50% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Metrillm

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  • Image
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  • Claude Prompts
    Reusable prompt templates, multi-step workflow chains, and quality gates for any MCP client.
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