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Dor1Toes

BackTest_MCP

by Dor1Toes

get_stock_data

Fetch OHLCV price bars for one or more symbols from local DB or remote source. Configure start/end dates and interval; get row counts or preview samples for backtesting.

Instructions

Fetch and cache OHLCV bars for one or more symbols from local DB or remote source.

Args: symbols: Non-empty list of ticker codes; pass a single symbol as ["600519"]. Symbol formats: quantforge://data/symbol-guide. start: Start date YYYY-MM-DD; empty uses a default lookback window. end: End date YYYY-MM-DD; empty defaults to today. interval: Bar interval (default "1d"). preview: When false, returns row counts only; when true, adds stats and head/tail samples. preview_rows: Number of head/tail rows per symbol when preview=True (default 3).

Returns: {"ok": true, "start": str, "end": str, "interval": str, "items": [...]} where each item has symbol, rows, source, and optional preview fields. On empty symbols: {"ok": false, "error": str}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
startNo
previewNo
symbolsYes
intervalNo1d
preview_rowsNo
Behavior4/5

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

With no annotations, the description discloses caching behavior, preview modes, return structure, and an error case for empty symbols. It also provides default values and source selection. Missing details like network access or permission requirements, but the coverage is strong for a data-fetching tool.

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?

The description is well-structured with Args and Returns sections, front-loads the purpose, and every sentence adds value. It is detailed yet concise, with no fluff.

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 6-parameter tool with no output schema and no annotations, the description provides a complete picture: purpose, all parameter semantics, return structure, and an error condition. It does not cover edge cases like invalid symbol format or start > end, but the core usage is clear.

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?

Schema descriptions are 0%, so the description fully carries parameter meaning. It explains every parameter: symbols format with an example, date formats and defaults, interval default, preview behavior, and preview_rows default. This is far beyond the basic schema.

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 first sentence states a specific action ('Fetch and cache OHLCV bars') on a specific resource ('one or more symbols') with source context ('local DB or remote source'). This clearly distinguishes it from backtest-related sibling tools, though it slightly overlaps with list_cached_symbols.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. While it explains parameter usage and defaults, it does not mention any exclusions or alternative tools, leaving the selection decision ambiguous given siblings like list_cached_symbols.

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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