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a-share-mcp

a-share-mcp

by shouldnotappearcalm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_hot_sectors

Retrieve hot A-share concept sectors, leading gainers, and trading volume to identify current market hotspots.

Instructions

    Fetches hot concept sectors with top gains.
    
    Returns top gaining concept sectors with leading stocks and trading volume.
    Data source: Eastmoney (东方财富).

    Args:
        top_n: Number of top sectors to return. Defaults to 20.
        format: Output format: 'markdown' | 'json' | 'csv'. Defaults to 'markdown'.

    Returns:
        Hot sectors data with: 板块名称, 涨跌幅, 龙头股, 龙头涨幅, 成交额

Input Schema

NameRequiredDescriptionDefault
top_nNo
formatNomarkdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It helpfully identifies the data source (Eastmoney) and return fields, but does not disclose freshness, ranking window, whether data is real-time or delayed, or any access/rate-limit behavior.

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 structure is clear and front-loaded with the purpose, then Args and Returns. There is some redundancy between the opening sentence and the Returns summary, but overall it is efficient.

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 two-parameter read tool with no output schema and no annotations, the description covers purpose, parameters, and return fields adequately. It is missing usage routing and ranking-period details, but nothing critical for basic invocation is absent.

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 coverage is 0%, but the description fully documents both parameters: top_n with its default of 20, and format with the allowed values 'markdown' | 'json' | 'csv' and its default. This adds substantial meaning beyond the bare schema.

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?

States a specific verb and resource ('Fetches hot concept sectors with top gains') plus the key returned content (leading stocks and trading volume). It is distinct from most siblings, though it does not explicitly differentiate itself from related tools like get_boards_summary or get_industry_data.

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?

Provides no explicit when-to-use guidance, no exclusions, and no named alternatives. The agent can infer that this is for hot concept sectors, but the description does not say when to prefer it over sibling sector/industry/board tools.

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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