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Axonity Flow MCP Server

Axonity Flow MCP Server

Official
by AxonityAI
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TypeScript
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restore_skill_version

Roll back a skill's draft to a previous version using the version's UUID. The current draft is snapshotted, preserving undo capability, while the live version remains unchanged.

Instructions

Roll a skill's DRAFT back to an earlier version. Takes the versionId (a UUID from list_skill_versions), NOT the checkpoint number. The current draft is snapshotted first, so this is itself undoable. It changes only the draft — what is live stays live until a human approves a new publish.

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": {
      "id": "<id>",
      "versionId": "<versionId>",
      "expectedVersion": "<expectedVersion>"
    },
    "name": "restore_skill_version"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe skill's id.
versionIdYesThe version row's UUID.
expectedVersionYesThe draft version you last read — 409 if stale.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description fully discloses that the current draft is snapshotted (making the operation undoable) and that only the draft is affected, not the live version. This exceptional transparency aids safe invocation.

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?

Four sentences, each with distinct value: action, parameter guidance, behavioral note, effect on draft vs. live. No redundant words.

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

Completeness4/5

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

The description explains the core mechanism and side effects, but does not describe the return format or error conditions (e.g., what happens on success/failure). Given the absence of an output schema, a bit more detail would be ideal.

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%, but the description adds meaning by explaining versionId is a UUID from list_skill_versions (not a checkpoint number) and implies the need for prior version listing, going beyond the schema's 'The version row's UUID'.

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 clearly states the action (roll back a draft) and the resource (skill's DRAFT), distinguishes it from sibling tools by specifying it affects only draft not live, and clarifies the parameter type (UUID from list_skill_versions).

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

Usage Guidelines4/5

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

It explicitly says when to use (roll back a draft) and hints at prerequisites (use versionId from list_skill_versions). It does not explicitly state when not to use, but the context is clear.

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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