# ai.quantifyme/quantifyme

- Registry name: `ai.quantifyme/quantifyme` · version 1.0.0
- Categories (automatic, keyword rules): Build & deploy, Developer tools, Markets & trading, Finance & markets
- Authentication at last check: No auth
- Declared risk: Has destructive tools (destructiveHint)
- Capabilities (declared by the server): experimental, prompts, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=ai.quantifyme%2Fquantifyme
- Server page: https://mcpstation.net/s/ai.quantifyme--quantifyme.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://mcp.quantifyme.ai/mcp`

## Tools (as reported by the server, not verified)

- `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: prompt: Natural-langua
- `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 summarize them — it d
- `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 works with NO signup:
- `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 \(GET /api/v1/wall
- `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 \(default Random Forest\). risk: NL risk rules \(e.
- `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 that exact strat
- `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 user wants to br
- `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\_shot\` as \`community\_
- `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 a bar that i
- `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 interactive \*\*ar
- `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 you can see wh

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html