MCP Tools Reference

This reference lists all 24 available MCP tools in Robonet's MCP server. Each tool includes its name, description, primary use case, and pricing tier.

Tool Categories

Data Access Tools - Browse strategies, symbols, indicators, data availability, and backtest results
AI-Powered Strategy Tools - Generate, optimize, and enhance strategies
Backtesting & Analysis - Test strategy performance on historical data
Prediction Market Tools - Build and test Polymarket strategies
Deployment Tools - Deploy and manage live trading agents
Account Tools - Manage credits and account information

Data Access Tools

Fast, low-cost tools for browsing and retrieving data. Execution time: <1s.

`get_all_strategies`

Description: Returns list of all your trading strategies with metadata.

Primary Use Case: Browse your strategy portfolio, see which strategies you've created.

Parameters:

include_latest_backtest (optional, boolean): Include latest backtest results for each strategy

Returns: List of strategies with names, components (base name, symbol, timeframe, risk level), and optionally latest backtest summaries.

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Use get_all_strategies with include_latest_backtest=true to see all my strategies and their recent performance

`get_strategy_code`

Description: Returns Python source code for a specified trading strategy.

Primary Use Case: View or analyze the implementation of an existing strategy.

Parameters:

strategy_name (required, string): Name of the strategy to retrieve

Returns: Python source code of the strategy file.

Pricing: Free

Example Usage:

Use get_strategy_code with strategy_name="MomentumBreakout_M" to see the implementation

`get_strategy_versions`

Description: Returns version history and metadata for a strategy lineage.

Primary Use Case: Track evolution of a strategy across versions (base → optimized → Allora-enhanced).

Parameters:

base_strategy_name (required, string): Base name of the strategy (without version suffixes)

Returns: List of all versions with creation dates and modification history.

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Use get_strategy_versions with base_strategy_name="MomentumBreakout" to see all versions

`get_all_symbols`

Description: Returns list of tracked trading symbols from Hyperliquid Perpetual.

Primary Use Case: Discover which trading pairs are available for backtesting and live trading.

Parameters:

exchange (optional, string): Filter by exchange name (default: all)
active_only (optional, boolean): Only return active symbols (default: true)

Returns: List of symbols with exchange, symbol name, active status, and backfill status.

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Use get_all_symbols with active_only=true to see which pairs I can trade

`get_all_technical_indicators`

Description: Returns list of 170+ technical indicators available in Jesse framework.

Primary Use Case: Discover which indicators you can use in your strategies (RSI, MACD, Bollinger Bands, etc.).

Parameters:

category (optional, string): Filter by category - momentum, trend, volatility, volume, overlap, oscillators, cycle, or all (default: all)

Returns: List of indicators with names, categories, and parameters.

Pricing: $0.001

Example Usage:

Use get_all_technical_indicators to see what indicators are available

`get_allora_topics`

Description: Returns list of Allora Network price prediction topics with metadata.

Primary Use Case: Discover which assets have ML prediction data available for strategy enhancement.

Parameters: None

Returns: List of topics with asset names, network IDs, and prediction horizons.

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Use get_allora_topics to see which assets have ML predictions available

`get_data_availability`

Description: Check available data ranges for crypto symbols and Polymarket prediction markets.

Primary Use Case: Verify data exists before running backtests to avoid failures due to missing data.

Parameters:

data_type (optional, string): Type of data to check - crypto, polymarket, or all (default: all)
symbols (optional, array): Specific crypto symbols to check (e.g., ["BTC-USDT", "ETH-USDT"])
exchange (optional, string): Filter crypto by exchange (e.g., "Binance Perpetual Futures")
asset (optional, string): Filter Polymarket by asset (e.g., "BTC", "ETH")
include_resolved (optional, boolean): Include resolved Polymarket markets (default: true)
only_with_data (optional, boolean): Only show items with available data (default: true)

Returns: Data availability including:

Symbol/market identification
Available date ranges (start/end dates)
Candle counts
Backfill status

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Check data availability for BTC-USDT to see the valid date range before backtesting

`get_latest_backtest_results`

Description: Returns recent backtest results from the database with performance metrics.

Primary Use Case: Quickly check strategy performance without running a new backtest.

Parameters:

strategy_name (optional, string): Filter by strategy name
limit (optional, integer, 1-100): Number of results to return (default: 10)
include_equity_curve (optional, boolean): Include equity curve timeseries data (default: false)
equity_curve_max_points (optional, integer, 50-1000): Maximum points for equity curve if included (default: 200)

Returns: List of backtest records with metrics including profit, drawdown, Sharpe ratio, trade statistics, and optionally equity curve.

Pricing: Free

Example Usage:

Use get_latest_backtest_results with strategy_name="MomentumBreakout_M" and limit=5 to see recent performance

AI-Powered Strategy Tools

Tools that use AI agents to generate, optimize, and enhance trading strategies. Execution time: 20-60s.

`create_strategy`

Description: Generate complete trading strategy code with AI based on your requirements.

Primary Use Case: Create a new strategy from scratch by describing your trading logic in natural language.

Parameters:

strategy_name (required, string): Name for the new strategy (e.g., "MomentumBreakout")
description (required, string): Detailed requirements including entry/exit logic, risk management, indicators

Returns: Complete Python strategy code implementing your requirements with entry/exit logic, position sizing, and risk management.

Pricing: Tier 4 - AI Generation (Real LLM cost + margin, max $4.50)

Execution Time: ~30-60s

Example Usage:

Use create_strategy to build a momentum strategy that:
- Enters long when RSI crosses above 30 and price breaks above 20-day MA
- Exits when RSI crosses below 70 or 3% stop loss is hit
- Uses 2% position sizing with 3x leverage
- Targets BTC-USDT on 1h timeframe

`generate_ideas`

Description: Creates innovative strategy concepts based on current Hyperliquid market data.

Primary Use Case: Get AI-generated strategy ideas when you're not sure what to build.

Parameters:

strategy_count (optional, integer, 1-10): Number of strategy ideas to generate (default: 1)

Returns: List of strategy concepts with descriptions of market conditions, logic, and rationale.

Pricing: Tier 4 - AI Generation (Real LLM cost + margin, max $1.00)

Execution Time: ~20-40s

Example Usage:

Use generate_ideas with strategy_count=3 to get three innovative strategy concepts

`optimize_strategy`

Description: Analyzes and improves strategy parameters using backtesting data and AI.

Primary Use Case: Tune indicator thresholds, risk settings, and entry/exit conditions for better performance.

Parameters:

strategy_name (required, string): Name of the strategy to optimize
start_date (required, string): Start date in YYYY-MM-DD format
end_date (required, string): End date in YYYY-MM-DD format
symbol (required, string): Trading pair (e.g., "BTC-USDT")
timeframe (required, string): Timeframe (1m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d)

Returns: Optimized strategy version with improved parameters and performance comparison.

Pricing: Tier 4 - AI Generation (Real LLM cost + margin, max $4.00)

Execution Time: ~30-60s

Example Usage:

Use optimize_strategy on "MomentumBreakout_h" for BTC-USDT 1h from 2024-01-01 to 2024-06-30

`enhance_with_allora`

Description: Adds machine learning price predictions from Allora Network to strategy logic.

Primary Use Case: Improve strategy performance by incorporating ML-based price forecasts as additional signals.

Parameters:

strategy_name (required, string): Name of the strategy to enhance
symbol (required, string): Trading pair (e.g., "BTC-USDT")
timeframe (required, string): Timeframe
start_date (required, string): Start date for comparison backtest
end_date (required, string): End date for comparison backtest

Returns: Enhanced strategy version with ML signals integrated, plus before/after performance comparison.

Pricing: Tier 4 - AI Generation (Real LLM cost + margin, max $2.50)

Execution Time: ~30-60s

Example Usage:

Use enhance_with_allora on "MomentumBreakout_h" for ETH-USDT 4h from 2024-01-01 to 2024-06-30

`refine_strategy`

Description: Apply iterative refinements to existing strategies with AI code editing.

Primary Use Case: Make targeted improvements or bug fixes to existing strategy code.

Parameters:

strategy_name (required, string): Strategy to refine (any version)
changes_description (required, string): What changes you want to make
mode (required, string): "new" (create new version) or "replace" (overwrite existing)

Returns: Refined strategy code with automatic validation and safety checks.

Pricing: Tier 4 - AI Generation (Real LLM cost + margin, max $3.00)

Execution Time: ~20-30s

Example Usage:

Use refine_strategy on "MomentumBreakout_h" to tighten stop loss from 3% to 2% and add trailing stop, mode="new"

Backtesting & Analysis

Compute-intensive tools for testing strategy performance on historical data. Execution time: 20-40s.

`run_backtest`

Description: Test strategy performance on historical data.

Primary Use Case: Validate strategy logic and measure performance before live deployment.

Parameters:

strategy_name (required, string): Name of the strategy to test
start_date (required, string): Start date in YYYY-MM-DD format
end_date (required, string): End date in YYYY-MM-DD format
symbol (required, string): Trading pair (e.g., "BTC-USDT")
timeframe (required, string): Timeframe (1m, 3m, 5m, 15m, 30m, 45m, 1h, 2h, 3h, 4h, 6h, 8h, 12h, 1D, 3D, 1W, 1M)
config (optional, object): Backtest configuration (fee, slippage, leverage, etc.)

Returns: Metrics including:

Performance: net_profit, total_return, annual_return, Sharpe ratio, Sortino ratio
Risk: max_drawdown, Calmar ratio, win_rate, profit_factor
Trade stats: total/winning/losing trades, streaks, average win/loss
Equity curve (downsampled to 200 points for visualization)

Pricing: Tier 3 - Compute ($0.001)

Execution Time: ~20-40s

Example Usage:

Use run_backtest on "MomentumBreakout_h" for BTC-USDT 1h from 2024-01-01 to 2024-12-31

Prediction Market Tools

Specialized tools for building and testing Polymarket prediction market strategies. Execution time varies.

`create_prediction_market_strategy`

Description: Generate Polymarket strategy code with YES/NO token trading logic.

Primary Use Case: Build strategies that trade on prediction market outcomes (e.g., election results, crypto prices).

Parameters:

strategy_name (required, string): Name for the strategy (e.g., "ValueBuyer")
description (required, string): Detailed requirements for YES/NO token logic and thresholds

Returns: Complete PolymarketStrategy code with should_buy_yes(), should_buy_no(), go_yes(), go_no() methods.

Pricing: Tier 4 - AI Generation (Real LLM cost + margin, max $4.50)

Execution Time: ~30-60s

Example Usage:

Use create_prediction_market_strategy to build a "PriceThreshold" strategy that:
- Buys YES tokens when price < 0.40 (undervalued)
- Buys NO tokens when price > 0.60 (overvalued)
- Exits positions when price returns to 0.45-0.55 range

`get_all_prediction_events`

Description: Returns tracked prediction events with their markets from Polymarket.

Primary Use Case: Browse prediction markets to find trading opportunities.

Parameters:

active_only (optional, boolean): Only return active events (default: true)
market_category (optional, string): Filter by category (e.g., "crypto_rolling", "politics", "economics")

Returns: List of prediction events with:

Event name and category
Associated markets with condition IDs
Market questions and outcomes (YES/NO)
Resolution status

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Use get_all_prediction_events with active_only=true to see current markets

`get_prediction_market_data`

Description: Returns prediction market metadata and YES/NO token price timeseries.

Primary Use Case: Analyze price history and trading patterns for a prediction market.

Parameters:

condition_id (required, string): Polymarket condition ID (from get_all_prediction_events)
start_date (optional, string): Filter candles from date (YYYY-MM-DD)
end_date (optional, string): Filter candles to date (YYYY-MM-DD)
timeframe (optional, string): Candle timeframe - 1m, 5m, 15m, 30m, 1h, or 4h (default: 1m)
limit (optional, integer): Maximum candles per token to return (default: 1000, max: 10000)

Returns:

Market metadata (question, outcomes, resolution status, asset, interval)
YES token price timeseries
NO token price timeseries

Pricing: Tier 1 - Data Access ($0.001)

Example Usage:

Use get_prediction_market_data with condition_id="0xb0eb..." and timeframe="1h" to analyze market

`run_prediction_market_backtest`

Description: Test prediction market strategy performance on historical market data.

Primary Use Case: Validate Polymarket strategy logic before live trading.

Parameters:

strategy_name (required, string): Name of the PolymarketStrategy
start_date (required, string): Start date in YYYY-MM-DD format
end_date (required, string): End date in YYYY-MM-DD format

For single-market backtest:

condition_id (string): Polymarket condition ID to test on

For rolling-market backtest:

asset (string): Asset symbol (e.g., "BTC", "ETH")
interval (string): Market interval (e.g., "15m", "1h")

Optional:

initial_balance (number): Starting USDC balance (default: 10000)
timeframe (string): Strategy execution timeframe (default: 1m)

Returns: Backtest metrics including profit/loss, win rate, and position history for YES/NO tokens.

Pricing: Tier 3 - Compute ($0.001)

Execution Time: ~20-60s

Example Usage:

# Single market backtest
Use run_prediction_market_backtest on "PriceThreshold" with condition_id="0x123..." from 2025-01-01 to 2025-01-30

# Rolling market backtest (multiple markets)
Use run_prediction_market_backtest on "PriceThreshold" with asset="BTC" interval="15m" from 2025-01-01 to 2025-01-30

Deployment Tools

Tools for deploying and managing live trading agents on Hyperliquid.

`deployment_create`

Description: Deploy a strategy to live trading on Hyperliquid.

Primary Use Case: Launch automated trading with your backtested strategy.

Parameters:

strategy_name (required, string): Name of strategy to deploy
symbol (required, string): Trading pair (e.g., "BTC-USDT")
timeframe (required, string): Candle interval (1m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d)
leverage (optional, number, 1-5): Position multiplier (default: 1)
deployment_type (optional, string): "eoa" (wallet) or "vault" (default: eoa)
vault_name (required for vault, string): Unique name for the Hyperliquid vault
vault_description (optional, string): Description for the vault

Returns: Deployment ID, status, wallet address, and configuration details.

Pricing: $0.50

Constraints:

EOA: Maximum 1 active deployment per wallet
Hyperliquid Vault: Requires 200+ USDC in wallet, unlimited deployments

Example Usage:

Deploy MomentumRSI_M to BTC-USDT on 4h timeframe with 2x leverage

`deployment_list`

Description: List all your deployments with status and performance metrics.

Primary Use Case: Monitor your live trading agents and their performance.

Parameters: None

Returns: List of deployments including:

Deployment ID and strategy name
Symbol, timeframe, leverage settings
Status (pending, running, stopped, failed)
Deployment type (EOA or Hyperliquid Vault)
Creation and stop timestamps
Hyperliquid stats (TVL, PnL, returns)

Pricing: Free

Example Usage:

List all my deployments to see their current status

`deployment_start`

Description: Start a stopped or failed deployment.

Primary Use Case: Resume trading with a previously stopped strategy.

Parameters:

deployment_id (required, string): ID of the deployment to start

Returns: Updated deployment status.

Pricing: Free

Note: Can only start deployments with status "stopped" or "failed".

Example Usage:

Start deployment 72130940-4136-497e-a92f-29bab22d73b2

`deployment_stop`

Description: Stop a running deployment.

Primary Use Case: Halt automated trading when needed.

Parameters:

deployment_id (required, string): ID of the deployment to stop

Returns: Updated deployment status.

Pricing: Free

Example Usage:

Stop my BTC-USDT deployment

Account Tools

Tools for managing credits and viewing account information.

`get_credit_balance`

Description: Get your current USDC credit balance.

Primary Use Case: Check available credits before running tools.

Parameters: None (requires authentication)

Returns:

balance_usdc: Current credit balance in USDC
wallet_address: Associated wallet address

Pricing: Free

Example Usage:

Check my credit balance

`get_credit_transactions`

Description: View credit transaction history with filtering and pagination.

Primary Use Case: Track credit usage and deposits.

Parameters:

limit (optional, integer, 1-100): Results per page (default: 20)
page (optional, integer): Page number, 1-indexed (default: 1)
transaction_type (optional, string): Filter by type - deposit, spend, withdraw, or refund

Returns: Paginated list of transactions with:

Transaction type and amount
Timestamp
Related tool or operation (for spend transactions)

Pricing: Free

Example Usage:

Show my recent credit transactions filtered by spend type

Pricing Tiers

Robonet uses a credit-based billing system with different pricing models:

Tier 1 - Data Access: $0.001 fixed cost for database queries
Tier 2 - Compute: $0.001 fixed cost for backtesting
Tier 3 - Deployment: $0.50 fixed cost for creating deployments
Tier 4 - AI Generation: Real LLM cost + margin (billed after execution)
- Uses actual Claude API costs plus platform margin
- Maximum price caps listed per tool
- Typical costs range from $0.50-$3.00 depending on complexity

Free Tools:

get_strategy_code - View strategy source code
get_strategy_versions - View version history
get_latest_backtest_results - View recent backtest records
deployment_list - List deployments
deployment_start - Start deployments
deployment_stop - Stop deployments
get_credit_balance - Check credit balance
get_credit_transactions - View transaction history

Note: Credits are reserved before tool execution and confirmed/cancelled after completion. Failed operations may incur partial costs for compute time used.

Common Workflows

1. Create and Test a New Strategy

1. generate_ideas (strategy_count=3) → Pick an idea
2. create_strategy (name + description) → Generate code
3. run_backtest (6 month period) → Test performance
4. If good: optimize_strategy → Tune parameters
5. If great: enhance_with_allora → Add ML signals
6. Final: run_backtest → Confirm improvements

2. Browse and Improve Existing Strategy

1. get_all_strategies (include_latest_backtest=true) → See portfolio
2. get_strategy_code (strategy_name) → Review implementation
3. refine_strategy (targeted changes, mode="new") → Make improvements
4. run_backtest → Validate changes

3. Explore Market Opportunities

1. get_all_symbols (active_only=true) → See available pairs
2. get_allora_topics → Check ML prediction availability
3. create_strategy → Build for chosen asset
4. enhance_with_allora → Add ML signals from start

4. Prediction Market Trading

1. get_all_prediction_events (active_only=true) → Find markets
2. get_prediction_market_data (condition_id) → Analyze market
3. create_prediction_market_strategy → Build YES/NO logic
4. run_prediction_market_backtest → Test on historical data

5. Deploy to Live Trading

1. get_all_strategies (include_latest_backtest=true) → Pick proven strategy
2. get_data_availability → Verify symbol has data
3. deployment_create (strategy, symbol, timeframe) → Deploy to Hyperliquid
4. deployment_list → Monitor status and performance
5. deployment_stop → Stop when needed

Tool Execution Times

<1s: All Data Access tools (get_* tools except AI tools)
~20-30s: Fast AI tools (refine_strategy, generate_ideas)
~20-40s: Backtesting tools (run_backtest, run_prediction_market_backtest)
~30-60s: AI strategy generation/optimization (create_strategy, optimize_strategy, enhance_with_allora, create_prediction_market_strategy)

Tips for Efficient Tool Usage

Start with Data Tools: Use get_all_strategies, get_all_symbols, get_allora_topics to understand what's available before generating new strategies.
Check Data Availability: Use get_data_availability before backtesting to verify data exists for your chosen symbol and date range.
Cost Management: AI tools (create_strategy, optimize_strategy, enhance_with_allora) use real LLM costs. Use generate_ideas first (cheaper) to explore concepts before committing to full implementation.
Iterative Development: Use refine_strategy for targeted changes instead of regenerating from scratch with create_strategy.
Backtest Early: Always run_backtest before optimize_strategy to ensure basic logic works.
Version Management: Use get_strategy_versions to track evolution and use mode="new" in refine_strategy to preserve working versions.
Prediction Markets: Check get_all_prediction_events regularly for new trading opportunities, and use get_prediction_market_data to analyze market dynamics before building strategies.
Live Trading: Use deployment_list to monitor your live agents, and deployment_stop if you need to halt trading quickly.

Security & Access Control

All MCP tools enforce wallet-based access control:

Strategy Ownership: Only the creating wallet can access, modify, or backtest a strategy
API Authentication: All tools require valid API key (JWT token) from Robonet backend
Credit Reservation: Credits are reserved atomically before execution to prevent TOCTOU issues
Input Validation: All parameters are validated and sanitized to prevent injection attacks

See MCP Server Setup for authentication configuration.

MCP Tools Reference ​

Tool Categories ​

Data Access Tools ​

get_all_strategies ​

get_strategy_code ​

get_strategy_versions ​

get_all_symbols ​

get_all_technical_indicators ​

get_allora_topics ​

get_data_availability ​

get_latest_backtest_results ​

AI-Powered Strategy Tools ​

create_strategy ​

generate_ideas ​

optimize_strategy ​

enhance_with_allora ​

refine_strategy ​

Backtesting & Analysis ​

run_backtest ​

Prediction Market Tools ​

create_prediction_market_strategy ​

get_all_prediction_events ​

get_prediction_market_data ​

run_prediction_market_backtest ​

Deployment Tools ​

deployment_create ​

deployment_list ​

deployment_start ​

deployment_stop ​

Account Tools ​

get_credit_balance ​

get_credit_transactions ​

Pricing Tiers ​

Common Workflows ​

1. Create and Test a New Strategy ​

2. Browse and Improve Existing Strategy ​

3. Explore Market Opportunities ​

4. Prediction Market Trading ​

5. Deploy to Live Trading ​

Tool Execution Times ​

Tips for Efficient Tool Usage ​

Security & Access Control ​

MCP Tools Reference

Tool Categories

Data Access Tools

`get_all_strategies`

`get_strategy_code`

`get_strategy_versions`

`get_all_symbols`

`get_all_technical_indicators`

`get_allora_topics`

`get_data_availability`

`get_latest_backtest_results`

AI-Powered Strategy Tools

`create_strategy`

`generate_ideas`

`optimize_strategy`

`enhance_with_allora`

`refine_strategy`

Backtesting & Analysis

`run_backtest`

Prediction Market Tools

`create_prediction_market_strategy`

`get_all_prediction_events`

`get_prediction_market_data`

`run_prediction_market_backtest`

Deployment Tools

`deployment_create`

`deployment_list`

`deployment_start`

`deployment_stop`

Account Tools

`get_credit_balance`

`get_credit_transactions`

Pricing Tiers

Common Workflows

1. Create and Test a New Strategy

2. Browse and Improve Existing Strategy

3. Explore Market Opportunities

4. Prediction Market Trading

5. Deploy to Live Trading

Tool Execution Times

Tips for Efficient Tool Usage

Security & Access Control