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InvestBrain

InvestBrain

by lj22503
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_record_decision

Records investment decisions with ticker, action, price, reason, and timestamp, confirming each entry to support disciplined tracking.

Instructions

Record an investment decision made by the user.

Args:
    data: Decision data including ticker, action (buy/sell/hold), price, reason, timestamp

Returns:
    dict: Confirmation of recorded decision

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": {
      "data": "<data>"
    },
    "name": "memory_record_decision"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
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 implies a mutation (recording) and mentions a confirmation return, but does not disclose persistence semantics, authentication needs, duplicate handling, or whether decisions are overwritten or appended.

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 core purpose, then structured Args/Returns. Efficient and readable, with no padding, though it does not encode any actionable constraints.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a nested-object write tool with no annotations and no output schema, the description is adequate but thin. It names the fields but omits required-vs-optional status, validation rules, and error behavior, leaving real gaps.

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% and the single parameter is an opaque nested object with additionalProperties=true. The description compensates by enumerating expected fields (ticker, action, price, reason, timestamp), adding real meaning beyond what the schema provides.

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+resource: 'Record an investment decision made by the user.' This clearly separates it from siblings like user_record_activity or thought_record_thought. However, it does not explicitly name what makes it distinct from those recording-style tools.

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?

No when-to-use guidance, no prerequisites, and no alternatives named. The agent must infer that this is for logging investment decisions and cannot tell from the description when it should prefer this over thought_record_thought or user_record_activity.

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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