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MCP Paradex Server

by sv

paradex_system_config

Read-only

Retrieve exchange-wide parameters including fee schedules, trading limits, and leverage settings to inform trading decisions.

Instructions

Understand the exchange's global parameters that affect all trading activity.

Use this tool when you need to:
- Check fee schedules before placing trades
- Verify trading limits and restrictions
- Understand exchange-wide parameters that affect your trading
- Keep up with changes to the exchange's configuration

This information provides important context for making trading decisions and
understanding how the exchange operates.

Example use cases:
- Checking current fee tiers for different markets
- Verifying maximum leverage available for specific markets
- Understanding global trading limits or restrictions
- Checking if any exchange-wide changes might affect your trading strategy

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
starknet_gateway_urlYes
starknet_fullnode_rpc_urlYes
starknet_fullnode_rpc_base_urlYes
starknet_chain_idYes
block_explorer_urlYes
paraclear_addressYes
paraclear_decimalsYes
paraclear_account_proxy_hashYes
paraclear_account_hashYes
oracle_addressYes
bridged_tokensYes
l1_core_contract_addressYes
l1_operator_addressYes
l1_chain_idYes
liquidation_feeYes

Implementation Reference

  • The main handler function 'get_system_config' decorated with @server.tool(name='paradex_system_config'). It fetches the system configuration from the Paradex API via the 'system/config' path, deserializes the response using SystemConfigSchema, and returns a SystemConfig Pydantic model.
    @server.tool(name="paradex_system_config", annotations=ToolAnnotations(readOnlyHint=True))
    async def get_system_config(ctx: Context) -> SystemConfig:
        """
        Understand the exchange's global parameters that affect all trading activity.
    
        Use this tool when you need to:
        - Check fee schedules before placing trades
        - Verify trading limits and restrictions
        - Understand exchange-wide parameters that affect your trading
        - Keep up with changes to the exchange's configuration
    
        This information provides important context for making trading decisions and
        understanding how the exchange operates.
    
        Example use cases:
        - Checking current fee tiers for different markets
        - Verifying maximum leverage available for specific markets
        - Understanding global trading limits or restrictions
        - Checking if any exchange-wide changes might affect your trading strategy
        """
        try:
            client = await get_paradex_client()
            response = await api_call(client, "system/config")
            system_config = SystemConfigSchema().load(response, unknown="exclude", partial=True)
            return system_config
        except Exception as e:
            await ctx.error(f"Error fetching system configuration: {e!s}")
            raise e
  • Tool registration via the @server.tool decorator with name='paradex_system_config' and readOnlyHint=True annotation.
    @server.tool(name="paradex_system_config", annotations=ToolAnnotations(readOnlyHint=True))
  • The tools/__init__.py imports the system module, which triggers the decorator-based registration of the 'paradex_system_config' tool on the server.
    from . import market, system, vaults
  • The server singleton is created in create_server() (line 78), and then tools are imported/registered via 'from mcp_paradex.tools import *' at line 82.
    def create_server() -> FastMCP:
        """
        Create and configure the FastMCP server instance.
    
        Returns:
            FastMCP: The configured server instance.
        """
        # Server metadata
        server_metadata: dict[str, Any] = {
            "name": config.SERVER_NAME,
            "description": "MCP server for Paradex trading platform",
            "vendor": "Model Context Protocol",
            "version": __version__,
        }
    
        # Create server instance
        server = FastMCP(
            name=config.SERVER_NAME,
        )
    
        return server
  • Test for paradex_system_config: defines SYSTEM_CONFIG_RESPONSE mock data and 'test_system_config_calls_correct_api_path' which verifies the tool calls client.get with the 'system/config' path.
    SYSTEM_CONFIG_RESPONSE = {
        "starknet_gateway_url": "https://alpha-mainnet.starknet.io",
        "starknet_fullnode_rpc_url": "https://rpc.mainnet.starknet.io",
        "starknet_fullnode_rpc_base_url": "https://rpc.mainnet.starknet.io",
        "starknet_chain_id": "0x534e5f4d41494e",
        "block_explorer_url": "https://starkscan.co",
        "paraclear_address": "0xabc",
        "paraclear_decimals": 8,
        "paraclear_account_proxy_hash": "0xdef",
        "paraclear_account_hash": "0x123",
        "oracle_address": "0x456",
        "bridged_tokens": [],
        "l1_core_contract_address": "0x789",
        "l1_operator_address": "0xabc",
        "l1_chain_id": "1",
        "liquidation_fee": "0.005",
    }
    
    
    async def test_system_config_calls_correct_api_path(mock_client):
        mock_client.get.return_value = SYSTEM_CONFIG_RESPONSE
    
        await server.call_tool("paradex_system_config", {})
    
        mock_client.get.assert_called_once_with(mock_client.api_url, "system/config", None)
Behavior4/5

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

Annotations already indicate readOnlyHint=true. The description adds context about the nature of the data (global parameters affecting trading) and usage scenarios (fee tiers, leverage, limits), enhancing transparency 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.

Conciseness4/5

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

The description is well-structured with bullet points and example use cases. It is slightly verbose but front-loaded with the main purpose, and every sentence adds value.

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 no parameters and an available output schema, the description sufficiently covers the tool's purpose and usage context. It could be more detailed about the output structure, but the schema handles that.

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

Parameters4/5

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

The input schema has no parameters, so schema coverage is 100%. The description goes beyond by explaining what the tool returns (global parameters) and when to use it, providing full context despite no parameters.

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: 'Understand the exchange's global parameters that affect all trading activity.' It is specific about the resource (global parameters) and distinguishes it from sibling tools focused on market data or trading operations.

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

Usage Guidelines4/5

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

The description provides explicit use cases (check fee schedules, verify limits, understand parameters) and example scenarios. It lacks explicit 'when not to use' guidance, but the context is clear enough to differentiate from siblings.

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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