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Gogi

by ai.gogi.api
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Stop Trading Instance

stop_instance

Stop automated execution for a running trading instance. Call list_instances first and confirm the target; stopping is reversible with start_instance and does not necessarily close existing broker positions. Use close_positions when the user explicitly wants exposure flattened.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesID of the trading instance to stop

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
messageNo
successNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedOutput schema / description
      Previous value: -"Structured Gogi result. Error responses include error and message fields."New value: +"Structured result. Error responses include error and message."
    • addedOutput schema / properties
      Added value: +{
+  "error": {
+    "type": "string"
+  },
+  "message": {
+    "type": "string"
+  },
+  "success": {
+    "type": "boolean"
+  }
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "Structured Gogi result. Error responses include error and message fields.",
+  "type": "object"
+}
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds substantive behavior beyond annotations: reversibility with start_instance, that existing broker positions are not necessarily closed, and the destination for exposure flattening. Annotations already cover safety hints (destructiveHint=false, idempotentHint=false), so the description appropriately focuses on the operational side effects and reversibility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each with clear purpose: purpose statement, prerequisite/alternative, and routing to close_positions. Front-loaded and no wasted words.

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?

Covers the action, prerequisite, reversibility, side effects on positions, and the alternative for exposure flattening. An output schema exists, so return values need not be explained; the description is complete for a 1-parameter tool.

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 single parameter is fully documented in the schema, so baseline is 3. The description does not add format or constraint details for instance_id beyond implying it must be confirmed via list_instances.

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 (stop) and resource (running trading instance) and clarifies that it stops automated execution, distinguishing it from close_positions and delete_instance. An agent can select it correctly without further inference.

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

Usage Guidelines5/5

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

Explicitly names alternatives (start_instance for reversal, close_positions for flattening exposure) and the conditions that select them. It also prescribes a prerequisite ('Call list_instances first and confirm the target'), which is strong usage guidance.

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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