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laogu-mcp

laogu-mcp

by laogu-caibao
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Risk Inputs

risk_inputs

Prepares financial risk assessment inputs by retrieving market snapshot and locating the latest periodic report from announcements, without scoring or recommendations.

Instructions

财务风险体检输入（对应 skill：laogu-risk 财务风险预警）。

返回：行情快照 + 最新一期定期报告定位（从公告列表找标题含"年度报告"/"半年度报告"/ "季度报告"的最新一条：art_code/标题/公告日期）。 输出契约：本 tool 只做输入准备，不打分；定期报告正文需再调 ann_content 获取； 报告期以公告标题为准，不推测。只陈述事实，不做买卖推荐。

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": {
      "code": "<code>"
    },
    "name": "risk_inputs"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose key behaviors: it is input-prep only, does not score, derives the report period strictly from the announcement title without speculation, and states facts only with no buy/sell recommendations. This is meaningful behavioral context beyond structured fields, though it says nothing about failure modes or rate limits.

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 front-loaded with purpose and returns, then the output contract, using a compact multi-part structure. It is dense but every clause (returns, contract, period rule, no-recommendation) contributes, so there is little waste.

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?

An output schema exists, so return-value format is covered, and the description adds the behavioral contract (no scoring, no speculation, facts only). The one missing piece is any meaning for the 'code' parameter, which leaves a small completeness gap for a 1-param tool with no annotations.

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

Parameters2/5

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

Schema description coverage is 0% and the single required parameter 'code' is never explained in the description. Context (market snapshot, financial reports) makes it inferable as a stock code, but the description does not compensate for the documentation gap as required at low coverage.

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

Purpose4/5

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

The description names a specific function (input preparation for a financial risk check, 'laogu-risk') and specifies its two concrete outputs: a market snapshot and the located latest periodic report. It also distinguishes its role from the sibling ann_content by noting the report body must be fetched separately. Clear verb+resource, though the tie to the 'risk_inputs' name relies on the 'laogu-risk' skill reference.

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?

It states this tool only prepares inputs and does not score, and explicitly names ann_content as the follow-up call needed for report text, which routes the agent across siblings. It does not enumerate when-not-to-use beyond the scoring disclaimer, so it stops just short of a full 5.

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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