Gogi

Update Saved Algorithm

update_algo

Update a saved algorithm owned by the user. Only supplied fields change. This edits the saved definition and does not change any already-linked live instance; use update_linked_algo for an instance-specific runtime change.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	No	Optional new algorithm name.
`algo_id`	Yes	Saved algorithm ID from list_my_algos.
`symbols`	No	Optional replacement symbols.
`timeframe`	No	Optional timeframe such as 1m, 1h, or 1d.
`description`	No	Optional new description.
`strategy_params`	No	Optional replacement strategy parameters.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`message`	No
`success`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 13, 2026

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"
+  }
+}

AddedSep 13, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnly=false, destructive=false, idempotent=false. The description adds genuinely useful context beyond them: 'Only supplied fields change' documents partial-update semantics, and it clarifies that the edit does not propagate to linked live instances. A 4 rather than 5 because no auth/permission or error behavior is mentioned.

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, zero waste, with the core purpose and partial-update behavior front-loaded before the sibling disambiguation. Every sentence earns its place.

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?

With an output schema present, annotations covering the safety profile, and 100% schema coverage, the description's remaining job is scope and sibling disambiguation, both of which it handles cleanly. Nothing an agent needs to invoke it correctly is missing.

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?

Schema coverage is 100%, so every parameter is already documented in the schema. The description adds the PATCH-style semantics ('only supplied fields change'), which explains why only algo_id is required, but provides no field-specific meaning beyond that. Baseline 3 is appropriate.

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 (update) and resource (saved algorithm) and scopes ownership ('owned by the user'). It explicitly distinguishes itself from the sibling update_linked_algo, so an agent can route correctly without opening either schema.

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?

Names the alternative (update_linked_algo) and the condition that selects it ('instance-specific runtime change'), and clarifies that this tool does not touch live instances. Both when-to-use and when-not-to-use are covered.

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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