Skip to main content
Glama
lj22503

InvestBrain

by lj22503
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

coaching_coaching_compare_history

Compare a current event against a saved historical scenario to spot shared variables, key differences, and whether past lessons apply. Use it to guide investment discipline.

Instructions

对比当前新事件与历史情景的变量结构异同。

从情景库中取出指定历史事件，用 LLM 对比分析：
- 相同点是什么
- 核心差异是什么
- 上次的教训是否适用于本次

Args:
    event: 当前新事件的描述（如"美联储降息50bp"）
    scenario_id: 要对比的历史情景 ID（从 list_coaching_scenarios 获取）

Returns:
    dict: {"status":"success", "comparison": "..."}

Input Schema

NameRequiredDescriptionDefault
eventYes
scenario_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It usefully discloses that the comparison is performed by an LLM and that the return value is a dict with status and comparison. It does not clarify read-only nature, side effects, permissions, or cost implications.

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 the comparison purpose and then uses a compact bullet list plus Args/Returns sections. It is efficient and well structured, with little wasted text.

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?

For a two-parameter comparison tool with no output schema and no annotations, the description covers what the tool does, both parameter meanings, and the return shape. Gaps remain around usage alternatives and behavioral safety context, but the core information needed to invoke it is present.

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 description coverage is 0%, so the description must compensate, and it does for both required parameters. It explains event as a description of the current new event with an example, and scenario_id as the historical scenario ID obtained from list_coaching_scenarios.

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 states a specific verb and resource: comparing a new event's variable structure against a historical scenario using LLM analysis. It is clear what the tool does, though it stops short of explicitly differentiating itself from other coaching-related siblings beyond pointing to list_coaching_scenarios as the source for IDs.

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

Usage Guidelines3/5

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

The description implies usage by saying to retrieve a historical event from the scenario library and comparing it, and it names list_coaching_scenarios as the source of scenario_id. However, it does not state when to use this tool versus alternatives like coaching_iterate_judgment or coaching_continue_coaching, nor does it give exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools