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kevynf

AKBridge MCP Server

by kevynf

macro_uk_unemployment_rate

Read-onlyIdempotent

Access UK unemployment rate figures via East Money, delivered as a pandas DataFrame for economic analysis.

Instructions

东方财富-经济数据-英国-失业率 https://data.eastmoney.com/cjsj/foreign_4_14.html :return: 失业率 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds minimal context: it returns a pandas DataFrame of unemployment rate from Eastmoney. No additional behavioral traits (e.g., pagination, data update frequency) are disclosed, but nothing contradicts the annotations.

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 compact, containing the source name, a URL, and a return specification. It is front-loaded with the purpose and does not waste words. However, the URL line is somewhat terse and could be considered extraneous, though it does add source credibility.

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?

With no parameters and no output schema, the description provides the essential return type and indicator, but it lacks details such as the frequency of the data (e.g., monthly, quarterly), the columns included in the DataFrame, or how the data is structured. Given the simplicity, this is adequate but not fully complete.

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 input schema has zero parameters, so the baseline is 4. The description correctly states the return type (pandas.DataFrame) and the content (unemployment rate), which is sufficient since there are no parameters to document.

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 tool as retrieving UK unemployment rate data from Eastmoney economic data. It names the specific indicator and source, distinguishing it from sibling tools like macro_uk_trade or macro_uk_bank_rate. However, it lacks an explicit verb like 'get' or 'fetch', relying on the tool name for the action.

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 explicit guidance on when to use this tool versus alternatives. The description only provides a source URL and return type, with no mention of when to prefer this over other UK macro indicators or any exclusions. Users must infer usage from the tool name.

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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