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nexus-xyz

nexus-exchange-mcp

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by nexus-xyz

fetch_positions

Retrieve the authenticated account's open positions with per-position risk details. Requires Nexus Exchange API credentials.

Instructions

Get the authenticated account's open positions. Positions carry per-position risk detail. notional_value, margin_used, roe, max_leverage, and leverage are derived from mirrored state, so any of them can be null with a companion <field>_error naming the reason (e.g. mark_price_unavailable); treat null as UNKNOWN, never as zero. leverage is currently always null (leverage_error: margin_state_not_mirrored) — do not infer it from margin_used. funding_paid is NOT one of those: it is always present and has no _error companion, so its "0" is a real zero (no funding accrued), not unknown. It is paid-positive — a negative value means funding was received. Requires API credentials.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.4.0

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses that derived fields can be null with companion <field>_error reasons, warns to treat null as UNKNOWN rather than zero, declares leverage is currently always null with the explicit error name, and documents funding_paid's sign convention and why its '0' is real. This is behaviorally rich information an agent cannot get elsewhere.

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?

Front-loaded with the purpose, then dense but purposeful detail. Every sentence about null-vs-zero and field error companions earns its place for a financial endpoint. It is on the long side, but not padded.

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?

With no output schema and no annotations, the description must carry the return-value burden, and it does explain the tricky fields (null/error pairing, leverage, funding sign). It stops short of enumerating the full position payload, but covers the highest-risk ambiguity.

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 tool takes zero parameters, so the baseline of 4 applies. There is nothing to document, and the description sensibly spends its words on return semantics instead.

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

Purpose4/5

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

States a specific verb and resource: 'Get the authenticated account's open positions.' That is unambiguous. However, it does not distinguish itself from the near-identical sibling get_positions (nor get_demo_positions / fetch_positions_history), so an agent cannot tell from the description which of the near-duplicate position tools to pick.

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

Usage Guidelines3/5

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

It states the precondition 'Requires API credentials', which is real guidance, and it clearly implies the use case (inspect my open positions). But it gives no when-to-use-vs-alternative guidance against the many sibling position/account tools, leaving the agent to infer.

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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