Describe a trading strategy in plain English and deploy a live signal model in one call. No signup.
Inferido de los transportes que declara este listado (streamable-http). Que un cliente no aparezca aquí no significa que se haya descartado: simplemente Forge no puede confirmarlo.
La verificación confirma la identidad del publicador (la propiedad del repo), no la seguridad del código. El análisis de seguridad cubre los CVE conocidos y los scripts de instalación sospechosos.
Leído de un handshake MCP real initialize → tools/list contra el endpoint declarado. No se invocó ninguna herramienta: tools/list es la llamada de introspección de solo lectura que el protocolo define para esto. Refleja lo que el servidor anunciaba en ese momento; un endpoint alojado no está fijado a ninguna versión y puede cambiar sin avisar.
https://mcp.quantifyme.ai/mcp13 herramientas · 2078 msone_shotEnd-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-language strategy description (e.g. "Buy when RSI < 30, sell > 70").
symbol: Currency pair to backtest on. One of: EURUSD, USDJPY, GBPUSD,
USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD.
timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min.
claude_model: Which Claude variant to use for code generation.
"sonnet" (default — best quality, 1/day free) or "haiku" (faster, 3/day free).
Ignored when `preset` is set (no generation needed).
preset: Curated winning-strategy slug. Skips Claude generation entirely
— deploys a pre-saved strategy known to backtest well on the chosen
symbol. Available slugs: ema_cross_fast, momentum, scalper_stack,
sma_only, trend_ema, volatility, bb_squeeze, all_mix, pivot_kid_ema.
Not every slug exists for every symbol — call list_models afterwards
to confirm what deployed.
community_id: Copy-trade a published community strategy. Pass the `id`
of an entry from `browse_community`. Loads that exact strategy code,
skips Claude generation, then trains + deploys it. `symbol`/`timeframe`
still apply to the backtest+deploy.
webhook_url: Optional webhook to receive live signals.
telegram_chat_id: Optional Telegram chat ID for signal delivery.
Returns IMMEDIATELY (the deploy runs in the background so the live card can
stream progress) with:
- job_token (str): pass to get_deploy_result to fetch the final result.
- poll_url (str): the card polls this for live progress; you can ignore it.
- pending (bool): always true here — the deploy is still running.
- symbol, timeframe (str).
Call this EXACTLY ONCE per request. Pass the user's words as `prompt`; do not
pre-pick presets/community strategies — the server routes (vague → a proven
community strategy, specific rules → a fresh generation).
NEXT STEP (always): call get_deploy_result(job_token) ONCE — it blocks until the
deploy finishes and returns the out-of-sample stats + `stem` + `source`/`author`
as TEXT so you can summarize. The live card already shows the chart, so you do
NOT need get_model_chart. If source='community', tell the user it used a
pre-existing strategy by @author and offer to generate a custom one.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-language strategy description (e.g. "Buy when RSI < 30, sell > 70"). symbol: Currency pair to backtest on. One of: EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD. timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min. claude_model: Which Claude variant to use for code generation. "sonnet" (default — best quality, 1/day free) or "haiku" (faster, 3/day free). Ignored when `preset` is set (no generation needed). preset: Curated winning-strategy slug. Skips Claude generation entirely — deploys a pre-saved strategy known to backtest well on the chosen symbol. Available slugs: ema_cross_fast, momentum, scalper_stack, sma_only, trend_ema, volatility, bb_squeeze, all_mix, pivot_kid_ema. Not every slug exists for every symbol — call list_models afterwards to confirm what deployed. community_id: Copy-trade a published community strategy. Pass the `id` of an entry from `browse_community`. Loads that exact strategy code, skips Claude generation, then trains + deploys it. `symbol`/`timeframe` still apply to the backtest+deploy. webhook_url: Optional webhook to receive live signals. telegram_chat_id: Optional Telegram chat ID for signal delivery. Returns IMMEDIATELY (the deploy runs in the background so the live card can stream progress) with: - job_token (str): pass to get_deploy_result to fetch the final result. - poll_url (str): the card polls this for live progress; you can ignore it. - pending (bool): always true here — the deploy is still running. - symbol, timeframe (str). Call this EXACTLY ONCE per request. Pass the user's words as `prompt`; do not pre-pick presets/community strategies — the server routes (vague → a proven community strategy, specific rules → a fresh generation). NEXT STEP (always): call get_deploy_result(job_token) ONCE — it blocks until the deploy finishes and returns the out-of-sample stats + `stem` + `source`/`author` as TEXT so you can summarize. The live card already shows the chart, so you do NOT need get_model_chart. If source='community', tell the user it used a pre-existing strategy by @author and offer to generate a custom one.
No se publicó ningún esquema de entrada para esta herramienta.
get_deploy_resultWait 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 does
NOT render another card (no need for get_model_chart). It BLOCKS until the deploy
finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it
again with the same token.
IMPORTANT: if `source == "community"`, the deploy used a PRE-EXISTING strategy by
`@author` — tell the user that, share the `live_url` as the Live dashboard link,
and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the
`note` field as your guide.
Args:
job_token: the token returned by `one_shot`.
Returns:
dict with: ok, stem, model, live_url, symbol, timeframe, channels (list),
stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE),
source ("community" | "generated"), author (community username if any),
author_url + strategy_url (render @author and "pre-existing strategy" as those
Markdown links), community_id, suggest_custom (bool), and note (a ready
instruction — follow it). On failure: {ok:false, error} (or {pending:true}).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 does NOT render another card (no need for get_model_chart). It BLOCKS until the deploy finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it again with the same token. IMPORTANT: if `source == "community"`, the deploy used a PRE-EXISTING strategy by `@author` — tell the user that, share the `live_url` as the Live dashboard link, and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the `note` field as your guide. Args: job_token: the token returned by `one_shot`. Returns: dict with: ok, stem, model, live_url, symbol, timeframe, channels (list), stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE), source ("community" | "generated"), author (community username if any), author_url + strategy_url (render @author and "pre-existing strategy" as those Markdown links), community_id, suggest_custom (bool), and note (a ready instruction — follow it). On failure: {ok:false, error} (or {pending:true}).
No se publicó ningún esquema de entrada para esta herramienta.
link_accountLink 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: it mints an anonymous trial
account per chat, so models deployed here belong to that throwaway identity
rather than to the user's real one. This returns a short-lived URL; once the
user opens it and approves, every later call in THIS chat acts as their
account and deploys land in their own model list.
Give the user the `link_url` to open. Nothing else is required from them.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: it mints an anonymous trial account per chat, so models deployed here belong to that throwaway identity rather than to the user's real one. This returns a short-lived URL; once the user opens it and approves, every later call in THIS chat acts as their account and deploys land in their own model list. Give the user the `link_url` to open. Nothing else is required from them.
No se publicó ningún esquema de entrada para esta herramienta.
list_modelsList the user's trained models with pre-computed train/test stats.List the user's trained models with pre-computed train/test stats.
No se publicó ningún esquema de entrada para esta herramienta.
list_deployedList the user's currently deployed (live) models.List the user's currently deployed (live) models.
No se publicó ningún esquema de entrada para esta herramienta.
top_upFund 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 for the POC. Returns your new credit balance.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 for the POC. Returns your new credit balance.
No se publicó ningún esquema de entrada para esta herramienta.
generate_strategyGenerate 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.g. "0.5% stop loss").
description: Optional one-line summary; treated as PRIMARY USER REQUEST.
symbol: Currency pair the code should target. One of: EURUSD, USDJPY,
GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD.
timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min.
claude_model: "sonnet" (default) or "haiku" (faster, higher daily cap).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.g. "0.5% stop loss"). description: Optional one-line summary; treated as PRIMARY USER REQUEST. symbol: Currency pair the code should target. One of: EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD. timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min. claude_model: "sonnet" (default) or "haiku" (faster, higher daily cap).
No se publicó ningún esquema de entrada para esta herramienta.
browse_communityBrowse 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 strategy in one call.
Args:
limit: How many top entries to return (default 20, max 200).
sort: Ranking metric, best-first — one of "composite" (default),
"ret" (total return), "wr" (win rate), "sharpe", "n_trades". Mirrors
the quantifyme.ai/community page's sortable columns, so an agent can
pick the top strategy by the metric it cares about in one call. The
sorted entries keep their `id`, so the winner is directly deployable
via one_shot(community_id=...).
Returns:
dict with:
- scripts (list[dict]): ranked entries, best first. Each has:
id (int — pass to one_shot as community_id), username, title,
description, created_at, score, and metrics {total_ret,
sharpe_strat, win_rate, n_trades, mdd, profit_factor}. SHOW the
top few with their win_rate / total_ret so the user can pick one.
- count (int).
- metric (str): the sort key applied.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 strategy in one call. Args: limit: How many top entries to return (default 20, max 200). sort: Ranking metric, best-first — one of "composite" (default), "ret" (total return), "wr" (win rate), "sharpe", "n_trades". Mirrors the quantifyme.ai/community page's sortable columns, so an agent can pick the top strategy by the metric it cares about in one call. The sorted entries keep their `id`, so the winner is directly deployable via one_shot(community_id=...). Returns: dict with: - scripts (list[dict]): ranked entries, best first. Each has: id (int — pass to one_shot as community_id), username, title, description, created_at, score, and metrics {total_ret, sharpe_strat, win_rate, n_trades, mdd, profit_factor}. SHOW the top few with their win_rate / total_ret so the user can pick one. - count (int). - metric (str): the sort key applied.
No se publicó ningún esquema de entrada para esta herramienta.
find_strategyFind 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".
CALL THIS FIRST when a user describes a strategy idea. Then present the match
(if any) and ASK which they want:
• Use it → one_shot(community_id=<match.community_id>) — deploys the
exact proven strategy (free, no generation).
• Generate fresh → one_shot(prompt="<their description>") — Claude writes a
brand-new custom strategy for them.
If there's no match, just offer to generate fresh.
Args:
description: the user's strategy idea in plain English (e.g. "buy EURUSD
15min when RSI < 30, sell when RSI > 70").
symbol: optional pair to constrain the match (EURUSD, USDJPY, GBPUSD,
USDCHF, USDCAD, AUDUSD, NZDUSD).
timeframe: optional granularity to constrain the match (1min/5min/15min/1h).
Returns:
dict with:
- match: the best existing strategy, or null. When present:
{community_id, title, username, wr, pf, ret, n_trades, symbol,
timeframe}. Pass community_id to one_shot to deploy it unchanged.
- description: echoed back — pass as one_shot(prompt=...) to generate fresh.
- suggestion: a ready-to-show sentence offering the user the choice.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". CALL THIS FIRST when a user describes a strategy idea. Then present the match (if any) and ASK which they want: • Use it → one_shot(community_id=<match.community_id>) — deploys the exact proven strategy (free, no generation). • Generate fresh → one_shot(prompt="<their description>") — Claude writes a brand-new custom strategy for them. If there's no match, just offer to generate fresh. Args: description: the user's strategy idea in plain English (e.g. "buy EURUSD 15min when RSI < 30, sell when RSI > 70"). symbol: optional pair to constrain the match (EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD). timeframe: optional granularity to constrain the match (1min/5min/15min/1h). Returns: dict with: - match: the best existing strategy, or null. When present: {community_id, title, username, wr, pf, ret, n_trades, symbol, timeframe}. Pass community_id to one_shot to deploy it unchanged. - description: echoed back — pass as one_shot(prompt=...) to generate fresh. - suggestion: a ready-to-show sentence offering the user the choice.
No se publicó ningún esquema de entrada para esta herramienta.
get_strategy_codeGet 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_id`. Read-only, no signup needed.
Args:
community_id: The `id` of a community entry (from `browse_community`).
Returns:
dict with: id, title, username, description, symbol, timeframe,
metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat},
and `code` (the full Python source). SHOW the code to the user, and offer
to deploy it via one_shot(community_id=...) or tweak it first.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_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.
No se publicó ningún esquema de entrada para esta herramienta.
get_quoteGet 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 is more than 15 minutes old.
Args:
symbol: G7 pair — one of EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD,
NZDUSD. Default EURUSD.
Returns:
dict with: symbol, price (latest close), time (bar timestamp, UTC),
change + change_pct (vs the prior 1-min bar), stale (bool).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 is more than 15 minutes old. Args: symbol: G7 pair — one of EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD. Returns: dict with: symbol, price (latest close), time (bar timestamp, UTC), change + change_pct (vs the prior 1-min bar), stale (bool).
No se publicó ningún esquema de entrada para esta herramienta.
get_model_chartVisualize 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 **artifact** from this tool's structured
output: a line chart of the cumulative return plus a table of the trades.
Args:
stem: The model stem (e.g. "14_EURUSD_15min_Model_24") from `list_models` / `one_shot`.
Returns:
dict with: ok, stem, symbol, timeframe, stats {ret, wr, pf, n, mdd, sharpe},
and trades [{type, entry_time, exit_time, entry_price, exit_price, pnl,
pnl_pct, exit_reason, period}] (most recent ~200). exit_reason is one of
TP / SL / close_only / signal / end. ret/mdd/wr are fractions; pnl_pct is percent.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 **artifact** from this tool's structured output: a line chart of the cumulative return plus a table of the trades. Args: stem: The model stem (e.g. "14_EURUSD_15min_Model_24") from `list_models` / `one_shot`. Returns: dict with: ok, stem, symbol, timeframe, stats {ret, wr, pf, n, mdd, sharpe}, and trades [{type, entry_time, exit_time, entry_price, exit_price, pnl, pnl_pct, exit_reason, period}] (most recent ~200). exit_reason is one of TP / SL / close_only / signal / end. ret/mdd/wr are fractions; pnl_pct is percent.
No se publicó ningún esquema de entrada para esta herramienta.
stream_testDiagnostic: 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 HTTP poll of /quote — each showing a live value
+ status, so you can see exactly which streaming mechanisms the client's
widget sandbox actually permits. Use when a live/ticking chart isn't moving.
Args:
symbol: G7 pair to stream (default EURUSD).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 HTTP poll of /quote — each showing a live value + status, so you can see exactly which streaming mechanisms the client's widget sandbox actually permits. Use when a live/ticking chart isn't moving. Args: symbol: G7 pair to stream (default EURUSD).
No se publicó ningún esquema de entrada para esta herramienta.
13 de 13 herramientas publicaron una descripción.
Los nombres y descripciones de las herramientas los escribe el publicador y se muestran literalmente como texto inerte. Son las cadenas que un cliente MCP pasa al modelo, así que Forge las analiza en busca de patrones de inyección de prompts — cualquier hallazgo aparece junto al análisis de seguridad de arriba. «Privilegiada» es una coincidencia de palabra clave en el nombre de la herramienta, no una auditoría de lo que hace: un nombre inofensivo puede hacer cualquier cosa.
Describe a trading strategy in plain English and deploy a live signal model in one call. No signup.
Los nombres enlazados abren el índice de Forge con todas las entradas que se observó que exponen esa herramienta. Ver todas las herramientas indexadas.
Esta entrada no publica ningún paquete de npm, así que Forge no tiene un árbol de dependencias para ella. Es una carencia de cobertura, no una afirmación de que no tenga dependencias.