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Habinar

MCP Paradex Server

by Habinar

paradex_filters_model

Retrieve schema information to build precise data filters for Paradex trading data. Understand available fields, data types, and formats to construct accurate filtering 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
NameRequiredDescriptionDefault
tool_nameYesThe name of the tool to get the filters for.

Implementation Reference

  • The handler function for the 'paradex_filters_model' tool. It is decorated with @server.tool(name="paradex_filters_model"), defining both the registration and implementation. Returns JSON schemas for other Paradex tools based on the input tool_name.
    @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]
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is a read-only metadata tool (implied by 'get detailed schema information'), but doesn't specify response format, error conditions, or performance characteristics. The description adds value by explaining the tool's role in filter construction, but lacks complete behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections: purpose statement, usage guidelines, and example use cases. While slightly verbose with 8 sentences, every sentence adds value by clarifying different aspects of when and how to use the tool. The front-loaded purpose statement immediately communicates the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (schema/metadata retrieval), no annotations, and no output schema, the description does a good job explaining the tool's purpose and usage context. It provides comprehensive guidance on when to use the tool and what benefits it offers, though it doesn't describe the return format or potential limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter 'tool_name', which is adequately described in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, maintaining the baseline score of 3 for high schema coverage. No additional syntax or format details are provided for the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get detailed schema information to build precise data filters' with specific verbs ('get', 'build', 'learn') and resources ('schema information', 'data filters'). It distinguishes itself from sibling tools by focusing on metadata/schema retrieval rather than actual data operations like trading or account management.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidelines with a bulleted list of specific scenarios: 'when you need to understand exactly what fields are available for filtering', 'learn the data types and formats', 'build complex JMESPath queries', and 'create sophisticated filtering and sorting expressions'. It clearly defines the tool's purpose as preparatory/metadata-focused rather than for actual data retrieval.

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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