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kevynf

AKBridge MCP Server

by kevynf

macro_euro_lme_holding

Read-onlyIdempotent

Get LME holding position reports from 2015 to present, detailing trader positions to support market analysis and decision-making.

Instructions

伦敦金属交易所(LME)-持仓报告, 数据区间从 20151022-至今 https://datacenter.jin10.com/reportType/dc_lme_traders_report https://cdn.jin10.com/data_center/reports/lme_position.json?_=1591533934658 :return: 伦敦金属交易所(LME)-持仓报告 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds the exact data source URLs and the date coverage, which is useful behavioral context beyond the annotations. It does not discuss rate limits or error behavior, but for a simple read-only data fetch this is acceptable.

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 concise and front-loaded with the core statement about LME position reports and the date range. It includes two source URLs and return-type annotations, which are slightly redundant but not excessive. The structure is clear and economical.

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 zero-parameter, read-only data retrieval tool, the description provides sufficient context: it names the data source, specifies the date range, and indicates the return type (DataFrame). It does not list DataFrame columns, but the topic is self-explanatory and the source URLs allow further investigation. The description is complete enough for an agent to use the tool correctly.

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 has zero parameters, so the input schema is empty and there is nothing to explain. Per the rubric, a baseline of 4 is appropriate when there are no 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 clearly states the tool provides LME (London Metal Exchange) position/holding reports, with a specific data range from 20151022 to present. It includes source URLs, which reinforces the resource type. It distinguishes from siblings like macro_euro_lme_stock by using '持仓' (position) rather than 'stock' (inventory).

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?

The description indirectly implies usage for retrieving LME position data, but it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusion criteria or mention sibling tools. The purpose is clear enough that an agent can infer its usage, but explicit guidance is missing.

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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