paradex_filters_model
Obtain schema details to construct accurate data filters, enabling precise data retrieval. Learn available fields, data types, and syntax for building complex JMESPath queries or multi-criteria expressions.
Instructions
Get detailed schema information to build precise data filters.
Use this tool when you need to:
- Understand exactly what fields are available for filtering
- Learn the data types and formats for specific fields
- Build complex JMESPath queries with correct syntax
- Create sophisticated filtering and sorting expressions
Knowing the exact schema helps you construct precise filters that
return exactly the data you need, avoiding trial and error.
Example use cases:
- Learning what fields exist in market data responses
- Finding the correct property names for filtering
- Understanding data types for numerical comparisons
- Building complex multi-criteria filters for large datasets
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tool_name | Yes | The name of the tool to get the filters for. |
Implementation Reference
- src/mcp_paradex/tools/market.py:25-56 (handler)The handler function for the 'paradex_filters_model' tool. It takes a tool_name parameter and returns the JSON schema for the specified tool's model using Pydantic model_json_schema(). Includes the registration decorator @server.tool.@server.tool(name="paradex_filters_model") async def get_filters_model( tool_name: Annotated[str, Field(description="The name of the tool to get the filters for.")], ) -> dict: """ Get detailed schema information to build precise data filters. Use this tool when you need to: - Understand exactly what fields are available for filtering - Learn the data types and formats for specific fields - Build complex JMESPath queries with correct syntax - Create sophisticated filtering and sorting expressions Knowing the exact schema helps you construct precise filters that return exactly the data you need, avoiding trial and error. Example use cases: - Learning what fields exist in market data responses - Finding the correct property names for filtering - Understanding data types for numerical comparisons - Building complex multi-criteria filters for large datasets """ tool_descriptions = { "paradex_markets": models.MarketDetails.model_json_schema(), "paradex_market_summaries": models.MarketSummary.model_json_schema(), "paradex_open_orders": models.OrderState.model_json_schema(), "paradex_orders_history": models.OrderState.model_json_schema(), "paradex_vaults": models.Vault.model_json_schema(), "paradex_vault_summary": models.VaultSummary.model_json_schema(), } return tool_descriptions[tool_name]
- src/mcp_paradex/tools/market.py:25-25 (registration)Registration of the paradex_filters_model tool using the @server.tool decorator.@server.tool(name="paradex_filters_model")
- Input schema definition for the tool using Annotated and Field for the tool_name parameter.tool_name: Annotated[str, Field(description="The name of the tool to get the filters for.")], ) -> dict: