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kevynf

AKBridge MCP Server

by kevynf

macro_usa_durable_goods_orders

Read-onlyIdempotent

Retrieve the monthly rate of US durable goods orders, with historical data from February 2008 to the present. Access the data as a structured DataFrame for analysis.

Instructions

美国耐用品订单月率报告, 数据区间从 20080227-至今 https://datacenter.jin10.com/reportType/dc_usa_durable_goods_orders :return: 美国耐用品订单月率报告 :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?

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds useful context: the exact data range (20080227-present), a source URL, and the return type (pandas.DataFrame), which go beyond the structured annotations and help set expectations about the output.

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

Conciseness3/5

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

The description is compact but contains redundancy: the ':return:' line repeats the report name from the first line. The URL is useful, but the docstring-style artifacts add unnecessary repetition, making it slightly less polished than a fully concise single-purpose sentence.

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

Completeness3/5

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

For a zero-parameter data retrieval tool, the description provides the essential facts: what the report is, the data range, the source, and the return type. However, it does not explain the meaning of '月率' (monthly rate) or outline the columns/format of the returned DataFrame, which would be helpful for an agent to interpret the results. It is adequate but not comprehensive.

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, and the schema coverage is 100% (vacuously). The baseline for 0 params is 4, and the description confirms the return type (pandas.DataFrame) which is the only additional semantic needed. No parameter explanation is required.

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?

The description clearly identifies the resource as the US durable goods orders monthly rate report, with a data range and source URL. However, it lacks an explicit action verb (e.g., 'retrieve' or 'get') and does not differentiate from the many sibling macro_usa_* tools beyond the title itself.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or related tools, leaving the agent to infer usage solely from the name and generic description.

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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