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deepq-financial-toolkit

deepq-financial-toolkit

by shenqingtech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

个股研报面：获取股票近90天（3个月）内最新的3篇研报观点

stockRepInsight

Extract recent analyst research reports for Chinese stocks to identify key investment perspectives and market insights from the past 90 days.

Instructions

个股研报面：获取股票近90天（3个月）内最新的3篇研报观点

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "stockRepInsight"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesA股股票名称或代码

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

C2.6/5.0
Behavior2/5

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. It mentions the time frame (90 days) and limit (3 reports), which are useful, but does not cover other critical aspects such as data source, update frequency, error handling, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.

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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose without any unnecessary words. It efficiently communicates the key information (action, resource, time frame, limit) in a compact form, making it easy for an AI agent to parse quickly.

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 moderate complexity (1 parameter, no annotations, but with an output schema), the description is minimally adequate. It covers the basic purpose and scope but lacks details on usage guidelines, behavioral traits, and output interpretation. The presence of an output schema reduces the need to describe return values, but overall completeness is limited, leaving room for improvement in contextual information.

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 the 'query' parameter documented as 'A股股票名称或代码' (A-share stock name or code). The description does not add any additional semantic details beyond this, such as examples or formatting requirements. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter documentation.

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 Guidelines2/5

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 does not mention any prerequisites, exclusions, or comparisons to sibling tools such as 'stkResearch' or 'industryResearch', which might offer similar or related functionality. This lack of contextual usage information limits its effectiveness for an AI agent in selecting the appropriate tool.

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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