get_strategy
Analyze prediction markets to generate trading strategies with causal decomposition, supporting platforms like Kalshi and Polymarket for informed decision-making.
Instructions
Run a full analysis and return a strategy signal with causal decomposition.
This is a blocking call that takes 30-90 seconds. For async control, use analyze_market + check_analysis_status + get_analysis instead.
Args: market_query: Description of the bet or market question to analyze. risk_limit: Reserved for position sizing constraints.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| market_query | Yes | ||
| risk_limit | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/rekko_mcp/server.py:256-271 (handler)The `get_strategy` tool is registered using `@mcp.tool()` and implemented as an asynchronous handler that makes an HTTP POST request to the `/v1/signals` endpoint.
@mcp.tool() async def get_strategy(market_query: str, risk_limit: float = 0.0) -> str: """Run a full analysis and return a strategy signal with causal decomposition. This is a blocking call that takes 30-90 seconds. For async control, use analyze_market + check_analysis_status + get_analysis instead. Args: market_query: Description of the bet or market question to analyze. risk_limit: Reserved for position sizing constraints. """ body: dict = {"market_query": market_query} if risk_limit > 0: body["risk_limit"] = risk_limit return await _request("POST", "/v1/signals", json=body)