MCP serverai.quantifyme/quantifyme

Describe a trading strategy in plain English and deploy a live signal model in one call.

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

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UNRATED 0.756

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mcp.quantifyme.ai/mcp

streamable-http

Says it is

quantifyme 0.1.0

protocol 2025-06-18

In the record since

32 days ago

Among servers18,413 with a card

0median 0.606 · this server 0.756 · highest on record 0.8561

Toolsfrom sha256:ab650101ba…b106be

The tools this server lists, read out of the definition it returned
Tool	Schema
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	input · output
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 th	input · output
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").	input · output
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 i	input · output
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	input · output
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-minut	input · output
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()` + `str	input · output
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	input · output
list_deployed List the user's currently deployed (live) models.	input · output
list_models List the user's trained models with pre-computed train/test stats.	input · output
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 commun	input · output
stream_test Diagnostic: test whether LIVE data streaming works in this client. Renders a widget with three panels — a JS timer (baseline), a WebSocket to the live price feed, and an H	input · output
top_up Fund your QuantifyMe credits with crypto (USDC) — no signup, no human, no card. The agent-native funding rail. SIMULATED in this build (no real charge) and capped at $100 f	input · output

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