Connect-MCP

ai.quantifyme/quantifyme

ai.quantifyme/quantifyme · v1.0.0

Describe a trading strategy in plain English and deploy a live signal model in one call. No signup.

MCPremoteno credentialsTransportMessaging

Reported outcomes

No reports yet.

Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 13 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 92/100

What it does: 13 tools · 10 read, 3 write (incl. delete/pay) · Developer tools, AI & LLM, Messaging

Test call list_models: worked (1,543 ms)

How the rating is made
  • Answers without login: +30
  • Speed of first answer: +10
  • Lists tools: +10
  • Tool descriptions: +15
  • Parameter descriptions: +2
  • Read/write annotations: +5
  • Safe test call: +20
  • More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL
https://mcp.quantifyme.ai/mcp

Tools

13 tools listed at last check
  • one_shot End-to-end deploy: generate strategy → train → deploy live. One of `prompt` (free-form NL), `preset` (curated winning strategy), or `community_id` (copy a published community strategy) is required. If more than one is passed, precedence is community_id > preset > prompt. Args:
  • get_deploy_result Wait for a `one_shot` deploy to finish and return its final result. `one_shot` returns a job_token immediately and the LIVE CARD already streams progress and renders the interactive backtest chart itself. Call this ONCE with the token to get the final numbers as TEXT so you can summariz
  • link_account Link this chat to the user's existing QuantifyMe account. Call this when the user says they already have a QuantifyMe account, or asks why their models are missing / where a model went, or wants what they deploy here to show up in their own dashboard. By default this connector work
  • list_models List the user's trained models with pre-computed train/test stats. `acting_as` names the identity listed: the linked account or a trial user (#1962).
  • list_deployed List the user's currently deployed (live) models.
  • top_up Simulated crypto top-up for a QuantifyMe DEV server only: no money moves, so it never makes an account paid and grants at most up to the free credit cap ($100 per call). quantifyme.ai refuses it (#1098) and answers with how to pay for real: send USDC on Base to your deposit address (
  • generate_strategy Generate Python strategy code (no training/deploy). Use when the user wants raw code. Args: features: NL description of features (e.g. "RSI 14, Bollinger Bands"). signals: NL description of signal logic (e.g. "Buy when RSI < 30"). model: ML model name (default Random Forest).
  • browse_community Browse the public community leaderboard of published strategies, ranked by a composite performance score (best first). No signup or key needed. Copy-trade flow: call this to find a top strategy, then pass its `id` to `one_shot` as `community_id` to deploy a live signal model running tha
  • find_strategy Find an existing PROVEN strategy that matches a plain-English idea, so you can offer the user a choice — deploy the existing one, or generate a fresh custom one. Mirrors the quantifyme.ai landing experience: "Found <X> by @<author> (WR/PF) — Use it / Generate fresh". Use this when a
  • get_strategy_code Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_sho
  • get_quote Get the latest price for a G7 FX pair — a quick "what's it at now" check. Useful for context before deploying a strategy. The price is the close of the most recent 1-minute bar from the platform's market feed (not a raw live tick); FX markets close on weekends, so the `stale` flag marks
  • get_model_chart Visualize a trained model's backtest — a cumulative-return chart + trade log + stats. Use after `one_shot` / `list_models` with the model's `stem` to SHOW the user how it traded (the "is it actually any good" view). In ChatGPT this renders an interactive widget. In Claude, render an int
  • stream_test Diagnostic: test whether LIVE data streaming works in this client. Renders a widget with three panels: a JS timer (baseline), the WebSocket panel (not offered: the feed needs a credential and none is sent to a widget), and an HTTP poll of /quote, each showing a live value + status, so

Badge

Connect-MCP badge for ai.quantifyme/quantifyme

Markdown for your README
[![Connect-MCP](https://clawy.uk/mcp/badge/ai.quantifyme/quantifyme.svg)](https://clawy.uk/mcp/s/ai.quantifyme/quantifyme)

Website