quantifyme
Server Details
Describe a trading strategy in plain English and deploy a live signal model in one call. No signup.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.4/5 across 5 of 5 tools scored.
Most tools have distinct purposes, but list_deployed and list_models could be confused without careful reading of descriptions. generate_strategy and one_shot both involve generation, but one_shot adds deployment, so they are sufficiently distinct.
Tool names follow a consistent verb_noun pattern in snake_case, with 'one_shot' being the only slight outlier as a fixed phrase. Overall naming is clear and predictable.
Five tools cover the core workflow of browsing, generating, training, and deploying strategies without being too few or too many. The count is well-scoped for the domain.
The tool surface covers main operations but lacks explicit tools for stopping/deleting deployed models or retrieving detailed model information. The missing lifecycle operations are notable gaps.
Available Tools
5 toolsbrowse_communityBrowse Community Strategies (leaderboard)ARead-onlyIdempotentInspect
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).
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).| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds further context: 'No signup or key needed', and details the return data structure including specific metrics, all consistent with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with Args and Returns sections, and concise overall. The meta-instruction to 'SHOW the top few...' adds slight verbosity but serves a clear purpose for the agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description adequately covers return values and provides a usage flow. It lacks error handling details but is complete for a read-only list tool with minimal parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description thoroughly explains the limit parameter: 'How many top entries to return (default 20, max 200)', adding meaning beyond the schema's minimal definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Browse the public community leaderboard of published strategies, ranked by a composite performance score' using a specific verb and resource, and distinguishes from siblings by mentioning the copy-trade flow and referencing one_shot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly outlines the copy-trade flow: 'call this to find a top strategy, then pass its id to one_shot as community_id', providing clear guidance on when to use this tool and how it connects to a sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_strategyGenerate Strategy Code (no training)AInspect
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).| Name | Required | Description | Default |
|---|---|---|---|
| risk | No | ||
| model | No | Random Forest | |
| symbol | No | ||
| signals | No | ||
| features | No | ||
| timeframe | No | ||
| description | No | ||
| claude_model | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and destructiveHint=false, and the description adds that the tool generates code (consistent). It does not provide deeper behavioral details (e.g., side effects, authentication needs, or rate limits), but with annotations present, the bar is lower.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a clear purpose sentence followed by an 'Args:' block listing parameters. It is front-loaded with the purpose. However, for 8 parameters it is relatively lengthy; a slightly more concise formatting could improve without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to explain return values. It thoroughly documents input parameters, including defaults and constraints. However, it lacks guidance on parameter interdependencies (e.g., whether certain combinations are required) or error handling, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries full burden. It adds extensive meaning for all 8 parameters, including natural language descriptions, defaults, and enum lists for symbol and timeframe, and clarifies that description is treated as the primary user request. This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Generate Python strategy code (no training/deploy)' with the verb 'generate' and resource 'strategy code'. It clearly distinguishes from sibling tools (browse_community, list_deployed, list_models, one_shot) which do not generate code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use when the user wants raw code', providing clear context for when to invoke this tool. It also implies not for training/deployment, though it does not explicitly exclude alternatives or mention specific when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_deployedList Deployed Live ModelsARead-onlyIdempotentInspect
List the user's currently deployed (live) models.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. Description adds specificity that results are scoped to the user's deployed models, which is useful context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise and front-loaded. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter tool with an output schema, description sufficiently covers purpose and scope. No additional context needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters present. Baseline score of 4 applies as description does not need to add param info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states action (list) and resource (user's currently deployed/live models). Distinguishes from sibling 'list_models' by specifying 'deployed (live)'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the term 'deployed (live)' but no explicit guidance on when to use vs alternatives like 'list_models' or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_modelsList Trained ModelsARead-onlyIdempotentInspect
List the user's trained models with pre-computed train/test stats.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavioral context beyond annotations by mentioning that the output includes pre-computed train/test stats. Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description complements them well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise, and front-loaded with the core purpose. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, robust annotations, and the existence of an output schema, the description is complete. It mentions the key extra detail (stats) that the agent needs to understand what the tool returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so the baseline score is 4 per guidelines. The description adds nothing about parameters, which is fine since none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action (List), resource (user's trained models), and additional detail (pre-computed train/test stats). The name and title are consistent, and it is distinct from sibling tools like list_deployed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage context by specifying the resource and output content, but does not explicitly state when to use this tool versus alternatives (e.g., list_deployed). No exclusion or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
one_shotDeploy Strategy (generate + backtest + go live)ADestructiveInspect
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:
dict with:
- live_url (str): tap-through to the deployed model on the qm dashboard.
- model, stem (str): model identifiers (e.g. "14_EURUSD_15min_Model_24").
- channels (list[str]): delivery channels active (e.g. ["webhook"]).
- train_stats (dict, optional): in-sample backtest metrics from the
training run — keys are some subset of {trades, win_rate,
profit_factor, return, sharpe, max_drawdown}. SHOW THESE IN YOUR
REPLY so the user immediately sees how the model backtested.
- progress (list[str]): per-stage progress log.| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | ||
| prompt | No | ||
| symbol | No | ||
| timeframe | No | ||
| webhook_url | No | ||
| claude_model | No | ||
| community_id | No | ||
| telegram_chat_id | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true. Description adds behavioral detail: skips Claude generation for preset and community_id, and returns progress logs and training stats. Does not disclose potential side effects like overwriting existing models, but adds sufficient context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured: one-line summary, precedence paragraph, organized 'Args:' list, and 'Returns:' section. Every sentence adds value; no fluff. Appropriate length given the tool's complexity (8 parameters, multiple modes).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (8 params, multiple modes, detailed return value) and that annotations exist, the description is complete. Covers all aspects: which parameters are mutually exclusive, return fields, and post-deployment verification (call list_models). The presence of an output schema reduces the burden, but the description still adds valuable context like the note about showing train_stats.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates. Each parameter is explained in detail: defaults, valid values (symbol, timeframe), precedence, and special behaviors (claude_model ignored when preset is set). The preset parameter even lists available slugs. This is exemplary parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'End-to-end deploy: generate strategy → train → deploy live.' This is a specific verb-resource combination that differentiates from siblings like generate_strategy (which only generates) and list_deployed (which lists deployments).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit precedence rules (community_id > preset > prompt) and explains when claude_model is ignored. Mentions checking list_models to confirm preset availability. However, does not explicitly state when NOT to use this tool (e.g., for backtesting without deployment).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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