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deepq-financial-toolkit

deepq-financial-toolkit

by shenqingtech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

A股市场温度计：A股当下市场的温度指数

aShareTemperature

Get the A-share market temperature index for specific dates to assess current market sentiment and conditions in China's stock market.

Instructions

A股市场温度计：A股当下市场的温度指数

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "aShareTemperature"
  }
}

Input Schema

NameRequiredDescriptionDefault
dateNo指定日期，默认当天

Output Schema

NameRequiredDescriptionDefault
msgYes
codeYes
dataNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • addedInput schema / $schema
      Added value: +"http://json-schema.org/draft-07/schema#"
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observed

TDQS

D1.9/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it offers no information about what the tool does operationally (e.g., whether it fetches, computes, or displays data), its data sources, update frequency, or output format. This lack of detail leaves the agent with no understanding of the tool's behavior beyond its vague name.

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

Conciseness2/5

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

The description is extremely concise but under-specified, not efficient. It consists of a single phrase that repeats the title without adding value, failing to use its limited space effectively. While it's front-loaded, it lacks any meaningful structure or informative content, making it inadequate rather than appropriately brief.

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?

Given the tool's low complexity (one optional parameter) and the presence of an output schema (which handles return values), the description's inadequacy is partially mitigated. However, with no annotations and a vague purpose, it still falls short of being complete. The output schema helps, but the description should clarify the tool's action and context to fully guide the agent.

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?

The input schema has 100% description coverage, with one parameter ('date') clearly documented in the schema. The description adds no parameter semantics beyond what the schema provides, but since schema coverage is high (>80%), the baseline score is 3. The description doesn't compensate for any gaps, but none exist due to the comprehensive schema.

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

Purpose2/5

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

Tautological: description restates name/title.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, nor does it reference sibling tools (e.g., aShareFearGreedIndex for sentiment or aShareMarketQuotes for price data). Without such information, an AI agent cannot determine appropriate usage scenarios.

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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