MCP server

Quantifyme Official

ai.quantifyme/quantifyme

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

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

Website

At a glance

Type

MCP server

Publisher

quantifyme.ai (verified by the MCP Registry)

Status

Working at last check

Tools

13

Token cost

About 4,507 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http quantifyme https://mcp.quantifyme.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.quantifyme]
url = "https://mcp.quantifyme.ai/mcp"

Gemini CLI

{"mcpServers":{"quantifyme":{"httpUrl":"https://mcp.quantifyme.ai/mcp"}}}

Cursor

{"mcpServers":{"quantifyme":{"url":"https://mcp.quantifyme.ai/mcp"}}}

VS Code

{"servers":{"quantifyme":{"type":"http","url":"https://mcp.quantifyme.ai/mcp"}}}

Antigravity

agy mcp add quantifyme https://mcp.quantifyme.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"quantifyme","server_url":"https://mcp.quantifyme.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"quantifyme","server_url":"https://mcp.quantifyme.ai/mcp"}

Tools

• browse_community Browses the web · ~452 tokens
  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 ~634 tokens
  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
• generate_strategy ~430 tokens
  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).
• get_deploy_result ~544 tokens
  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
• get_model_chart ~310 tokens
  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
• get_quote ~234 tokens
  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_strategy_code ~282 tokens
  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
• link_account ~306 tokens
  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_deployed ~50 tokens
  List the user's currently deployed (live) models.
• list_models ~78 tokens
  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).
• one_shot ~831 tokens
  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:
• stream_test ~196 tokens
  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
• top_up ~160 tokens
  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 (

Browse

• Claude tools
• Running code
• Crypto and blockchains

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