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lolifamily

ashare-mcp

get_stock_snapshot

Get a stock's latest price, valuation, industry, total shares, and net profit in a single request. Works for indices too, returning price and valuation data with nulls for unavailable fields.

Instructions

One-call snapshot: latest price, valuation, industry, total shares, net profit.

Combines data from K-line, stock_industry, and profit_data.

Designed for individual stocks. An index (sh.000300, etc.) has K-line + valuation but no industry / basic / profit rows; those fields come back null instead of aborting the call, so price + PE/PB/PS are still returned.

Profit fields (net_profit, revenue) come from the most recently published report, so they may be Q1, H1, 9M, or FY depending on the calendar. Always inspect profit_period_type ('Q1'/'H1'/'9M'/'FY') and profit_period_months (3/6/9/12) before comparing across companies: baostock reports these cumulative-from-year-start, so Q1's net_profit is 3 months, NOT comparable to FY's 12. To annualize, multiply Q1 by 4, H1 by 2.

baostock fills MBRevenue only on H1 / FY reports, so revenue is null when profit_period_type is 'Q1' or '9M'.

Args: code: Stock code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
Behavior5/5

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

With no annotations provided, the description fully carries the burden. It details data source combination, fallback behavior for indices, and critical nuances about profit period types, cumulative reporting, and revenue null cases.

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?

Well-structured with front-loaded purpose, data sources, special behavior, and detailed caveats. Every sentence adds value, but the length might be slightly dense; a bit more conciseness could be achieved.

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

Completeness5/5

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

Given one parameter, no output schema, and no annotations, the description provides comprehensive context: input, data sources, behavior for different inputs, meaning of output fields, and important usage caveats.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, and the description adds a minimal arg description 'Stock code.' This is adequate but not exceptional; the description compensates slightly but could add more detail (e.g., format examples).

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 it's a 'one-call snapshot' combining data from multiple sources, listing the specific fields. It distinguishes itself from siblings by being a composite tool, and the name reinforces its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says designed for individual stocks, explains behavior for indices (null fields but still returns). Implicitly suggests when to use instead of multiple individual calls, but lacks explicit when-not or alternatives compared to siblings.

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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