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deepq-financial-toolkit

deepq-financial-toolkit

by shenqingtech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript

金融实体解析：根据自然语言中提及的个股、基金、概念板块、行业，返回其代码、名称。其中，个股还返回所属概念、所属申万行业，ETF基金返回其跟踪指数

finEntityExtract

Extract financial entities from natural language queries to identify stocks, funds, sectors, and concepts with their codes and attributes.

Instructions

金融实体解析：根据自然语言中提及的个股、基金、概念板块、行业，返回其代码、名称。其中，个股还返回所属概念、所属申万行业，ETF基金返回其跟踪指数

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": "finEntityExtract"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes包含股票代码、名称的自然语言问句

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. It mentions the tool extracts entities and returns specific data, but lacks details on behavioral traits like error handling, rate limits, authentication needs, or whether it's read-only or mutative. This is a significant gap for a tool with no annotation coverage.

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 description is a single, efficient sentence that front-loads the core functionality. It avoids redundancy but could be slightly more structured by separating key points, though it remains appropriately sized.

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?

Given the tool's complexity (extracting multiple entity types with varied returns), the description covers the purpose and output details well. With an output schema present, it doesn't need to explain return values, and the single parameter is fully documented in the schema, making it reasonably complete despite gaps in usage guidelines and behavioral transparency.

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 description coverage is 100%, with the parameter 'query' documented as '包含股票代码、名称的自然语言问句'. The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 where the schema does the heavy lifting.

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 prerequisites, exclusions, or compare it to siblings such as guessStockCode or guessFundCode, leaving the agent without context for tool selection.

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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