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Interactive Brokers MCP Server

Interactive Brokers MCP Server

by code-rabi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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get_account_info

Retrieve your Interactive Brokers account information and balances using a simple confirmation input.

Instructions

Get account information and balances. Usage: { "confirm": true }.

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": {
      "confirm": "<confirm>"
    },
    "name": "get_account_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
confirmYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.21.3
  2. Removedv1.15.1
  3. Changed3 schema fields changedv1.0.0
    • addedInput schema / properties / confirm
      Added value: +{
+  "const": true,
+  "type": "boolean"
+}
    • removedInput schema / properties / random_string
      Removed value: -{
-  "description": "Dummy parameter for no-parameter tools",
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "random_string"
-]New value: +[
+  "confirm"
+]
  4. First observed

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as whether confirmation is required for safety, side effects, or permissions needed. The example implies a confirmation step but does not explain its 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 two sentences, concise and front-loaded with the purpose. However, it could be slightly expanded without losing conciseness to include more context or usage hints.

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

Completeness2/5

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

Given the lack of output schema and annotations, the description is too brief. It does not specify what account information or balances are returned, prerequisites, or any other details necessary for an agent to use the tool confidently.

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

Parameters3/5

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

The schema has 0% description coverage for parameters, so the burden falls on the description. The description shows the usage format with `confirm: true`, adding minimal meaning beyond the schema's const requirement. It does not explain why confirm is needed or any constraints.

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?

The description explicitly states 'Get account information and balances', which is a specific verb and resource. This clearly distinguishes it from sibling tools like get_positions and get_order_status, which focus on different aspects.

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?

The description provides a usage example but gives no guidance on when to use this tool versus alternatives. With many sibling tools, this omission makes it difficult for an AI agent to select the correct tool without additional context.

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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