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RedSamerai

a-stock-mcp

by RedSamerai

Ipo Analyzer

ipo_analyzer

Identify current A-share IPO subscription opportunities. Get details like issue price, subscribe date, exchange, and board to evaluate new listings.

Instructions

新股申购分析:当前可申购新股列表。

Returns

dict { "count": int, "list": [{"symbol", "name", "subscribe_code", "issue_price", "subscribe_date", "exchange", "board"}, ...], "error": str | None, }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It discloses the return structure including the error field, which is helpful, but it does not state whether the operation is read-only, whether any authentication is needed, or how current the 'current' list is. For a read-only list tool this is acceptable but not richly transparent.

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

Conciseness5/5

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

The description is compact and front-loaded with the core purpose, followed by a clear structured return format. Every line earns its place, and the format is scannable for an agent deciding whether to call the tool.

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 the tool's low complexity, zero parameters, and a provided output schema, the description is complete enough for correct invocation. It specifies the output shape, key fields, and error slot, which covers what an agent needs to interpret the result.

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 input schema is already fully self-describing with no properties, so there is little parameter burden on the description. The description adds value by enumerating the return fields, which is the closest analog to parameter/documentation semantics here.

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 both the domain ('新股申购分析') and the concrete deliverable ('当前可申购新股列表'), making it evident that the tool returns currently subscribable IPO stocks. This distinguishes it from siblings like north_flow_tracker or scan_limit_up_stocks, which cover different market signals.

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

Usage Guidelines3/5

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

The description implies the usage context: call when you need the current list of IPO subscriptions. However, it provides no explicit guidance about when not to use this tool or how it compares to alternatives among the sibling tools, so the routing is left somewhat to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.