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Axonity Flow MCP Server

Axonity Flow MCP Server

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by AxonityAI
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TypeScript
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read_persona_published

Retrieve the live published version of a persona to compare with its draft and preview pending changes.

Instructions

Read the LIVE published version of a persona — what the runtime actually uses, as opposed to the draft that read_persona returns. Diff the two to see what a pending publish would change.

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>"
    },
    "name": "read_persona_published"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe persona's id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description must convey behavioral traits. It correctly identifies this as a read operation and specifies the resource (published persona). However, it omits details like authentication requirements, error conditions, or what exactly is returned (e.g., full persona object). Sufficient but not comprehensive.

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?

Two sentences with zero waste. The first sentence clearly names the action and resource, the second adds a usage hint (diffing). Front-loaded and efficient.

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?

Given the tool's simplicity (one parameter, no output schema), the description covers the core purpose and differentiator. It lacks details about return values, but the primary use case is well-addressed. Slightly more completeness would be ideal (e.g., describing that it returns the full persona object), but it is still fairly complete.

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% with a clear 'id' description. The tool description does not add extra meaning to the parameter beyond what the schema already provides. Baseline score of 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?

The description clearly states the tool reads the live published version of a persona, uses a specific verb ('Read'), and explicitly distinguishes itself from the sibling 'read_persona' which returns the draft. This differentiation is precise and immediately understandable.

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?

The description explicitly contrasts this tool with read_persona (draft) and suggests using 'Diff the two' to see pending changes. It indicates when to use (live version) but does not explicitly state when not to use or list all alternatives. Still, clear context is provided.

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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