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KyuRish

trading212-mcp-server

fetch_historical_order_data

Read-onlyIdempotent

Retrieve historical order data including filled, cancelled, and rejected orders with execution details. Filter by ticker and paginate results to analyze past trade execution quality.

Instructions

Retrieve past orders (filled, cancelled, rejected) with execution details,
fill prices, and timestamps. Supports pagination and ticker filtering.

Use this to review trade history or to analyze past execution quality.
For currently active orders, use fetch_all_orders instead.

Args:
    cursor: Pagination cursor from a previous response. Omit for the first page.
    ticker: Filter results to a specific instrument (e.g., 'AAPL_US_EQ'). Omit for all.
    limit: Number of orders per page, 1-50. Defaults to 20.

Returns:
    List of HistoricalOrder with ticker, type, status, filledQuantity, fillPrice,
    dateCreated, dateExecuted, and more

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cursorNo
tickerNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Description adds pagination behavior and filtering details beyond annotations. No contradictions, and annotations already cover safety hints.

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?

Well-organized with purpose statement, Args section, and Returns section. No unnecessary words, every sentence adds value.

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

Completeness5/5

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

Covers all essential aspects: purpose, parameters, pagination, filtering, return fields. No gaps for an AI agent to misuse.

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

Parameters5/5

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

All three parameters are fully explained with usage context (cursor pagination, ticker example, limit range and default) despite 0% schema coverage.

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?

Clearly states 'Retrieve past orders' with specific scope (execution details, fill prices, timestamps) and differentiates from sibling fetch_all_orders.

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?

Explicitly says to use for trade history/analysis and advises using fetch_all_orders for active orders.

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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