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InvestBrain

InvestBrain

by lj22503
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

coaching_start_coaching

Start investment coaching by analyzing a user's investment question and automatically choosing a 10-step research analysis for simple queries or a Socratic guided dialogue for complex ones.

Instructions

启动学习辅导，分析投资问题。

自动判断问题是简单还是复杂：
- 简单问题（如"该不该买XX"）→ 直接输出 10 步标准投研分析
- 复杂问题（如"为什么XX和YY走势背离"）→ 启动 Socratic 多轮引导对话

Args:
    user_input: 用户提出的投资问题（如"茅台2000块值得买吗"）
    mode: 强制模式，"simple" 或 "complex"，留空则自动判断

Returns:
    simple 模式: {"status":"success", "mode":"simple", "answer": "...", "scenario_id":"..."}
    complex 模式: {"status":"success", "mode":"complex", "session_id":"...", "question":"...", "options":[...]}

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": {
      "user_input": "<user_input>"
    },
    "name": "coaching_start_coaching"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNo
user_inputYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does well on output shape for both modes, but omits that complex mode starts a persistent session (implied only by session_id), says nothing about permissions, cost/latency, or whether state is created and how it is torn down.

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?

Front-loaded with the action and the branching rule, then structured Args/Returns. Every line adds information; the only mild bloat is the conventional docstring headers rather than prose.

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?

No output schema exists, and the description compensates by enumerating the return payloads for both modes (status/mode/answer/scenario_id vs. session_id/question/options). Combined with the mode-selection logic, an agent has enough to call and interpret results correctly.

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?

Schema coverage is 0% and the schema declares no enum for mode, so the description must compensate: it explains user_input with a concrete example and enumerates the legal mode values ('simple' or 'complex') plus the default-by-omission behavior. That closes the biggest gap, though it doesn't cover input length or language expectations.

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?

States a specific verb and resource (启动学习辅导/start coaching on an investment question) and immediately distinguishes two operating modes with concrete examples ('该不该买XX' → simple, '为什么XX和YY走势背离' → complex). An agent can tell exactly what this does and how its behavior branches.

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?

Gives clear context for when each behavior fires and how to force it via mode; the auto-detection rule is explicit. It does not name the follow-up siblings (continue_coaching, abandon_coaching) that a complex-mode session implies, so routing after invocation is left implicit.

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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