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Habinar

MCP Paradex Server

by Habinar

paradex_vault_account_summary

Check vault trading account status, monitor margin, exposure, and risk metrics to inform trading decisions and manage positions effectively.

Instructions

Get a comprehensive overview of a vault's trading account status.

Use this tool when you need to:
- Check account health and available margin
- Monitor total exposure and leverage
- Understand risk metrics and account status
- Assess trading capacity before placing new orders
- Get a consolidated view of account performance

This provides essential information about account standing and
trading capacity to inform risk management decisions.

Example use cases:
- Checking available margin before placing new orders
- Monitoring account health during market volatility
- Assessing total exposure across all markets
- Understanding maintenance margin requirements
- Planning position adjustments based on account metrics

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vault_addressYesThe address of the vault to get account summary for.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The primary handler function for the paradex_vault_account_summary tool. It accepts a vault_address parameter, calls the Paradex API via api_call to retrieve the account summary, validates the JSON response using a Pydantic TypeAdapter for list[VaultAccountSummary], handles errors, and returns the validated summary list.
    @server.tool(name="paradex_vault_account_summary")
    async def get_vault_account_summary(
        vault_address: Annotated[
            str, Field(description="The address of the vault to get account summary for.")
        ],
    ) -> list[VaultAccountSummary]:
        """
        Get a comprehensive overview of a vault's trading account status.
    
        Use this tool when you need to:
        - Check account health and available margin
        - Monitor total exposure and leverage
        - Understand risk metrics and account status
        - Assess trading capacity before placing new orders
        - Get a consolidated view of account performance
    
        This provides essential information about account standing and
        trading capacity to inform risk management decisions.
    
        Example use cases:
        - Checking available margin before placing new orders
        - Monitoring account health during market volatility
        - Assessing total exposure across all markets
        - Understanding maintenance margin requirements
        - Planning position adjustments based on account metrics
        """
        try:
            client = await get_paradex_client()
            response = await api_call(
                client, "vaults/account-summary", params={"address": vault_address}
            )
            if "error" in response:
                raise Exception(response["error"])
            results = response["results"]
            summary = vault_account_summary_adapter.validate_python(results)
            return summary
        except Exception as e:
            logger.error(f"Error fetching account summary for vault {vault_address}: {e!s}")
            raise e
  • Pydantic BaseModel defining the output schema for the tool response. Specifies fields like address, deposited_amount, vtoken_amount, total_roi, total_pnl, and created_at with descriptions and types.
    class VaultAccountSummary(BaseModel):
        """Model representing an account summary for a vault."""
    
        address: Annotated[str, Field(description="Contract address of the vault")]
        deposited_amount: Annotated[
            str, Field(description="Amount deposited on the vault by the user in USDC")
        ]
        vtoken_amount: Annotated[str, Field(description="Amount of vault tokens owned by the user")]
        total_roi: Annotated[
            str, Field(description="Total ROI realized by the user in percentage, i.e. 0.1 means 10%")
        ]
        total_pnl: Annotated[str, Field(description="Total P&L realized by the user in USD")]
        created_at: Annotated[
            int, Field(description="Unix timestamp in milliseconds of when the user joined the vault")
        ]
  • Pydantic TypeAdapter used in the handler to validate the API response as a list of VaultAccountSummary objects.
    vault_account_summary_adapter = TypeAdapter(list[VaultAccountSummary])
  • Decorator that registers the get_vault_account_summary function as an MCP tool with the specified name.
    @server.tool(name="paradex_vault_account_summary")
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool as providing 'essential information' and a 'consolidated view,' implying it's a read-only operation for monitoring, but doesn't explicitly state behavioral traits like whether it requires authentication, has rate limits, or returns real-time vs. cached data. It adds some context about risk management but lacks operational details.

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 appropriately sized and front-loaded, starting with the core purpose, followed by usage guidelines and examples. It avoids redundancy, but the bullet points and example sections are slightly verbose; every sentence earns its place by reinforcing usage contexts, though it could be more streamlined.

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 complexity (monitoring account status) and the presence of an output schema (which covers return values), the description is largely complete. It explains the tool's purpose, usage, and context well, but without annotations, it lacks explicit behavioral disclosures like safety or performance traits, leaving a minor gap.

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%, with the single parameter 'vault_address' well-documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints. Since the schema handles the parameter documentation adequately, the baseline score of 3 is appropriate.

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 a comprehensive overview of a vault's trading account status,' specifying the verb 'get' and resource 'vault account summary.' It distinguishes from siblings like paradex_vault_balance or paradex_vault_positions by emphasizing a holistic view including health, margin, exposure, and risk metrics, not just isolated data.

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 explicitly lists five bullet points for when to use this tool (e.g., 'Check account health and available margin,' 'Monitor total exposure and leverage'), and includes example use cases that reinforce these guidelines. It implicitly distinguishes from siblings by focusing on comprehensive status rather than specific components like fills or positions, though it doesn't name alternatives directly.

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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