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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_prompt_snippet_published

Fetch the live published version of a prompt snippet to compare with its draft and preview pending changes before publishing.

Instructions

Read the LIVE published version of a prompt_snippet — what the runtime actually uses, as opposed to the draft that read_prompt_snippet 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_prompt_snippet_published"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe prompt_snippet's id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.1/5.0
Behavior3/5

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

Since no annotations are provided, the description bears full responsibility. It discloses that the tool reads the published version (read-only), but doesn't detail any other behavioral traits like auth requirements or rate limits. The contrast with the draft version is useful context.

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?

The description is two sentences long, front-loaded with the purpose, and efficiently introduces the use case of diffing. 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?

For a simple read tool with one parameter and no output schema, the description provides sufficient context. It defines the tool's purpose, distinguishes it from a related tool, and suggests a practical use case.

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?

There is one parameter (id) with 100% schema description coverage. The description adds no additional meaning beyond what the schema provides, so it meets the baseline for high coverage.

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 prompt_snippet, contrasting with the draft version from read_prompt_snippet. It uses specific verbs and resources, and distinguishes from the sibling tool read_prompt_snippet.

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 mentions the alternative read_prompt_snippet and suggests diffing to see pending publish changes. While it doesn't provide explicit when-not-to-use conditions, the context is clear and the usage is well-defined.

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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