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Axonity Flow MCP Server

Axonity Flow MCP Server

Official
by AxonityAI
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TypeScript
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create_output_schema

Define the structure of output data by creating an output schema draft with camelCase fields; the backend validates and returns the schema's ID and version.

Instructions

Create a new output_schema draft. Pass the entity's fields (camelCase) in fields; the backend validates them. Returns the created output_schema with its id and 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": {
      "fields": "<fields>"
    },
    "name": "create_output_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
fieldsYesThe output_schema's fields as a JSON object (camelCase keys), e.g. { "name": "…", "description": "…" }.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It mentions 'draft' and validation but does not disclose error handling, authentication needs, rate limits, or any side effects beyond creation. This is insufficient for a mutation tool.

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: first states the primary action, second details the parameter and return. No unnecessary words. Efficient and front-loaded.

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 single parameter and no output schema, the description adequately explains what the tool does and returns. However, it could mention that the schema is a 'draft' (implies not published) and maybe clarify that an existing output schema might be required for updates. Still, it is nearly complete for this simple tool.

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?

The description adds value beyond the input schema by specifying camelCase keys and that the backend validates them. The schema already has a descriptive comment and example, so the description reinforces key semantics. Baseline 3 is exceeded due to this extra context.

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: 'Create a new output_schema draft.' It specifies the required parameter (fields in camelCase), backend validation, and return value (id and version). This distinguishes it from sibling tools like update_output_schema or read_output_schema.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., existing output schema) or when not to use it (e.g., for updates). The description is purely functional without contextual recommendations.

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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