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InvestBrain

InvestBrain

by lj22503
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_get_behavior_patterns

Retrieve identified behavior patterns from a user's investment history, like buying on price drops or stopping losses early, to improve investment discipline.

Instructions

Get identified behavior patterns from user's investment history.

Returns:
    list: Behavior patterns like "tends to buy on price drops", "stops loss too early"

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "memory_get_behavior_patterns"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It helpfully discloses the shape and flavor of the return value with concrete examples, but says nothing about whether these patterns are precomputed or derived on demand, what happens when no history exists, or any auth/permission requirement.

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?

Short and front-loaded: the purpose sentence comes first, followed by a compact return description. The 'Returns:' block is slightly code-doc flavored but earns its place given there is no output schema.

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 zero-param read tool with no output schema, describing the return content with examples is exactly the right compensation. It could go further on empty-state or refresh behavior, but nothing essential for invocation is missing.

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?

The tool takes zero parameters, so the schema baseline is 4. There is nothing further for the description to clarify about inputs, and it adds nothing misleading.

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?

States a specific verb and resource ('Get identified behavior patterns from user's investment history'), which is clear on its own. However, it does not distinguish itself from similarly named siblings like pattern_get_pattern_summary or pattern_get_pattern_report, leaving an agent to guess which one to pick.

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?

There is no when-to-use guidance, no prerequisites, and no reference to any alternative tool. The agent is told what it returns but not under what circumstances to call it instead of the pattern_* siblings.

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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