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kevynf

AKBridge MCP Server

by kevynf

macro_china_gdzctz

Read-onlyIdempotent

Retrieve China urban fixed asset investment statistics from Eastmoney to analyze macroeconomic trends, returning structured pandas DataFrame results.

Instructions

东方财富-中国城镇固定资产投资 https://data.eastmoney.com/cjsj/gdzctz.html :return: 东方财富中国城镇固定资产投资 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is fully covered. The description's only added behavioral fact is the rtype pandas.DataFrame, which tells the agent the return shape; it does not disclose data frequency, update cadence, or history span. With annotations carrying the main burden, a 3 is appropriate.

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?

It is short, but poorly structured: the resource name is stated twice (title line and :return: line) and the URL and rtype are dumped without front-loading any actionable framing. It is efficient in length but redundant in content.

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 macro-indicator fetch with no output schema, the description plus annotations give enough to invoke it: what it returns (a DataFrame of China urban fixed asset investment) and that it is a safe read. The rtype substitutes for an output schema. Only the lack of data frequency/coverage detail keeps it from a 5.

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 takes zero parameters, so there is nothing for the description to disambiguate beyond what the empty schema already implies. Baseline 4 applies for parameterless tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource, China urban fixed asset investment from East Money, and provides a source URL, so an agent can tell what data it returns. However, it contains no verb and it essentially restates the tool name/title rather than describing the retrieval action, and it does nothing to distinguish this indicator from the many sibling macro_china_* indicators (e.g. macro_china_real_estate, macro_china_construction_index). Purpose is inferable but vague.

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 when-to-use guidance, no exclusions, and no mention of alternative or related indicators among the numerous siblings. The agent is left to infer entirely from the name that this fetches this one specific indicator.

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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