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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_policy_published

Read the live published version of a policy to see what the runtime uses. Compare it with the draft to identify pending changes before publishing.

Instructions

Read the LIVE published version of a policy — what the runtime actually uses, as opposed to the draft that read_policy 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_policy_published"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe policy's id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that this retrieves the live published version (as opposed to draft) and implies it is a read-only operation. Could be more explicit about safety, but the purpose is clear.

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, front-loaded with main action, no redundant information. Every sentence adds value.

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?

For a simple read with one parameter and no output schema, the description adequately explains what the tool does and how it differs from the draft version. Could mention return format but not necessary.

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 description 'The policy's id.' Description does not add any extra meaning beyond what the schema already provides, so baseline 3.

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?

Description clearly states it reads the LIVE published version of a policy, contrasting with the draft returned by read_policy. Specific verb and resource, effectively distinguishes from the sibling tool read_policy.

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 says when to use this tool (to see the runtime version) and when not (use read_policy for draft). Suggests diffing the two to preview pending publish changes, giving clear context.

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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