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Axonity Flow MCP Server

Axonity Flow MCP Server

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by AxonityAI
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read_tool

Retrieve a tool by its ID, including the current draft and version, to review the latest state before making updates.

Instructions

Read one tool by id, including its current draft and version. Read before you update — you need the current version.

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_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe tool'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?

No annotations are provided, so the description must convey behavioral traits. It indicates this is a read operation and mentions it includes draft and version, but it does not explicitly state that it is safe, idempotent, or has no side effects. The implication is sufficient for a simple read tool, but it could be more explicit.

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: the first states the purpose clearly, and the second provides actionable guidance. There is no extraneous information, and every sentence serves a purpose.

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 is complete. It explains what the tool does, what data it returns, and when to use it, fulfilling all necessary context for an AI agent to select and invoke it correctly.

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?

The input schema has 100% coverage with a single parameter 'id' described as 'The tool's id.' The description adds no additional meaning beyond the schema, so it meets the baseline score of 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?

The description clearly states the action ('Read'), the resource ('one tool by id'), and the included data ('current draft and version'). It effectively distinguishes from sibling tools like 'list_tools' and other read operations by specifying the resource type.

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 advises using this tool before updating ('Read before you update — you need the current version'). This provides clear context for when to use it, though it does not explicitly list alternative tools or cases when not to use it.

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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