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by io.github.saloprj
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agents_update_from_template

Update a forked agent's instructions (prompt) to the latest version of the system template it was created from.

Use when the platform has improved a template and the user wants their forked agent to pick up the new prompt. This OVERWRITES the agent's prompt_text with the template's current prompt — any customizations to the prompt are replaced (recoverable via prompt history). Tool/model/execution settings are NOT changed. Only works on agents forked from a template (not from-scratch agents or templates themselves).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesID of the forked agent to update from its template
in_workspaceNoRun this one call in this workspace id instead of the session's. Nothing is stored; other sessions are not affected.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / in_workspace
      Added value: +{
+  "description": "Run this one call in this workspace id instead of the session's. Nothing is stored; other sessions are not affected.",
+  "type": "integer"
+}
  2. Added
  3. Removed
  4. Added

TDQS

A4.9/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing what is destroyed (prompt_text overwritten, customizations replaced), the recovery path (prompt history), and what is untouched (tool/model/execution settings). The 'recoverable via prompt history' detail is fully consistent with destructiveHint=false.

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?

Purpose is front-loaded in the first sentence, followed by when-to-use and then the destructive/scope caveats. Every sentence carries distinct information; nothing is redundant.

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?

For a mutation tool, the critical behavioral facts (what changes, what is preserved, recoverability, applicability scope) are all covered, and the annotations already carry the safety profile. No output schema is needed, and nothing an agent requires to call it correctly 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?

Schema coverage is 100%, so both parameters are already documented. The description still adds a meaningful constraint on the agent_id parameter — it must reference a forked agent, not a from-scratch agent or a template — which sharpens selection beyond the raw schema.

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 (a forked agent's instructions/prompt) and a precise target (the latest version of the system template it was created from). This is clearly distinguishable from siblings like agents_update or agents_prompt_restore.

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?

Explicit when-to-use ('when the platform has improved a template and the user wants their forked agent to pick up the new prompt') plus an explicit when-not ('only works on agents forked from a template — not from-scratch agents or templates themselves'). Nothing is left to inference.

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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