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get_my_order_records

Read-only

Use this when the user asks what orders were placed through MCPify (our audit records, including which source placed them: web, mcp agent, admin) — successes and rejections alike.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of records
actionNoFilter by action

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and non-destructive behavior. The description adds that the tool returns audit records including successes and rejections and shows the placing source, which helps the agent set expectations about the data scope beyond the annotation's safety profile.

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

Conciseness5/5

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

A single sentence that leads with the trigger condition and packs in the key scoping details (MCPify audit, source types, successes/rejections). No filler or repetitive content.

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?

For a read-only list tool with two optional parameters and no output schema, the description adequately conveys what the records are and when to use it. It could be slightly more explicit about the return shape, but the term 'records' and the audit context are sufficient for correct invocation.

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%, so the input schema already documents both limit and action parameters. The description does not add any parameter-specific meaning, meeting the baseline for schema-covered 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 uses a specific verb and resource: retrieving order records placed through MCPify, and explicitly defines the scope as audit records with source attribution (web, mcp agent, admin). This clearly differentiates it from siblings like get_my_trade_history.

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 states exactly when to use the tool ('Use this when the user asks what orders were placed through MCPify'). It provides clear context but does not explicitly name alternative tools or state when not to use it, so it stops short of a full when/when-not distinction.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool maps to a distinct resource/action: market data, account state, resting orders, audit records, fills, order placement, and risk management. The only close pair, preview_order and place_order, is clearly delineated as validation vs execution.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (get_, place_, cancel_, close_, set_). The object names are uniform and predictable, making the set easy to navigate.

Tool Count5/5

13 tools is well-scoped for a trading server: market data, account/order queries, execution, and position risk each have coverage without redundancy or bloat.

Completeness4/5

The surface covers the core trading lifecycle: market data, account, preview/place/cancel, close position, TP/SL, and historical records. Minor gaps exist (e.g., no modify-order operation and no standalone position detail endpoint), but agents can work around them via cancel/replace and get_my_account.

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