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Ai Model Experiments MCP server

online · 100% uptime
by pipeworx-io·io.github.pipeworx-io/ai-model-experiments·v0.2.1

AI Model Experiments MCP ('Model Lab') — run the same prompts across many AI models simultaneously…

B77/100grade B
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Ai Model Experiments tools

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How Ai Model Experiments compares

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

Safety score
77/100
higher than 50% of 4,460 AI and agents servers
Uptime
100%
of our recent checks; the typical remote AI and agents server answers 100%
Response time
154 ms
faster than 76% of 2,331 AI and agents servers

Release history: 6 versions in the registry since 2026-09-11, latest 2026-10-30, typically one every 6 days.

Install Ai Model Experiments in Claude Code, Cursor or VS Code

claude mcp add --transport http ai-model-experiments https://gateway.pipeworx.io/ai-model-experiments/mcp
Add to Cursor

What the publisher says

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

@pipeworx/ai-model-experiments

Model Lab — run the same prompt(s) across many AI models at once (Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Qwen and more via OpenRouter) and compare their outputs, cost, and latency side by side, with an optional AI-written comparison summary when the run completes.

Part of Pipeworx — an MCP gateway connecting AI agents to 1558+ live data sources.

Tools

  • experimentmodels(search?, mincontext?, maxpricepermtok?, limit?) — browse ~300 available model ids with context window and our billed per-token price (provider cost × 1.5). Use the returned ids in experimentcreate.
  • experiment_estimate(prompts[], models[], reps?, params?, summary?) — free dry-run: cell count + estimated billed cost range for a spec, before creating it.
  • experimentcreate(name?, prompts[], models[], reps?, params?, summary?, maxspendusd) — creates and starts an experiment (prompts × models × reps). Async: returns experimentid immediately; execution happens on a separate cron worker within ~1 minute. Requires prepaid balance ≥ maxspendusd.
  • experimentstatus(experimentid) — cell counts by state, spend vs cap, whether complete. Poll this after create.
  • experimentresults(experimentid, include_outputs?) — per-model aggregates (latency, tokens, cost, error rate), per-cell outputs, and the AI-written comparison summary.
  • experiment_list(limit?) — caller's experiments, newest first.
  • experimentcancel(experimentid) — skips pending cells (unbilled); in-flight cells finish and bill.
  • experimenttopup(amountusd?) — current balance + the x402 top-up flow.

Auth

Prepaid only — no free tier, no BYO-key mode. Every call to experimentcreate requires a Pipeworx platform credit balance ≥ maxspendusd (1 credit = $0.0001). Top up via x402: POST https://gateway.pipeworx.io/credits/topup?amountusd=N returns an HTTP 402 payment challenge (USDC on Base); retry with a PAYMENT-SIGNATURE header to settle, and credits land instantly. See experiment_topup for the exact flow and current balance.

Billing is provider cost × 1.5, floored at $0.10/experiment. Model prices returned by experiment_models are already the billed (marked-up) price, never the raw provider cost.

Execution is handled by a separate cron worker (workers/experiment-runner, fires every minute) — a created experiment is not synchronous. Poll experimentstatus rather than expecting experimentcreate to block until done.

Data sources

  • — the model catalog (id, context window, provider pricing) that experiment_models wraps and re-prices.
  • Inference for each cell is executed against OpenRouter's chat completions API by workers/experiment-runner, which also reads OpenRouter's /generation endpoint for the actual per-call cost so the 1.5× markup is billed on real cost, not an estimate.

Full build plan, architecture, and current live-vs-planned status: docs/model-lab-plan.md.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "ai-model-experiments": {
      "url": "https://gateway.pipeworx.io/ai-model-experiments/mcp"
    }
  }
}

What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/ai-model-experiments/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — askpipeworx, discovertools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

Shortened. The full README is on GitHub.

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

Ai Model Experiments: common questions

Is Ai Model Experiments MCP server safe?
Mostly: it is graded B (77/100). Read the Ai Model Experiments safety report
How do I install Ai Model Experiments?
It runs remotely at gateway.pipeworx.io. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does Ai Model Experiments need an API key?
Yes. The registry entry asks for PIPEWORX_API_KEY.
Is Ai Model Experiments maintained?
The last commit was 40 days ago (2026-09-25). The latest release is v0.2.1.
Is Ai Model Experiments up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of Ai Model Experiments?
Servers from other publishers that do the same job: LLM Sandbox MCP server, Agent-Native Analytics MCP server and Image MCP server.
How does Ai Model Experiments compare with other AI and agents MCP servers?
safety score 77/100 (higher than 50% of 4,460 AI and agents servers); uptime 100% (of our recent checks; the typical remote AI and agents server answers 100%); response time 154 ms (faster than 76% of 2,331 AI and agents servers). Full comparison.

Alternatives to Ai Model Experiments

Same job from other publishers: the closest match first, then the best rated.

  • LLM Sandbox
    Securely run LLM-generated code in isolated containers across 7 languages and 3 container backends.
    A
  • Agent-Native Analytics
    Agent-Native Amplitude/Mixpanel - connect data sources, prompt for charts
    A
  • Image
    AI image generation and editing with prompt optimization and quality presets
    B
  • llmtrim
    MCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.
    A
  • Agent Lsp
    Orchestrates language servers into 65 code-intelligence tools across 32 languages, token-optimized.
    A

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