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Axonity Flow MCP Server

Axonity Flow MCP Server

Official
by AxonityAI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

create_workflow

Create a new workflow draft by providing its fields as a JSON object. Returns the created workflow with its unique ID and version.

Instructions

Create a new workflow draft. Pass the entity's fields (camelCase) in fields; the backend validates them. Returns the created workflow 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_workflow"
  }
}

Input Schema

NameRequiredDescriptionDefault
fieldsYesThe workflow'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

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that a draft is created and that the backend validates fields, but it does not describe error handling, idempotency, or permissions required.

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 three concise sentences with no extraneous information. It front-loads the purpose and immediately provides actionable guidance on the parameter.

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 lack of output schema and no annotations, the description adequately covers the creation intent, field format, and return values. It could mention failure modes or prerequisites, but overall it is sufficient for an agent to use the tool correctly.

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?

With 100% schema coverage, the description adds value by providing an example of the fields parameter format (camelCase JSON object with example keys), going beyond the schema's description.

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's action ('Create a new workflow draft') and distinguishes it from siblings like update_workflow, read_workflow, and delete_workflow by implying creation of a new entity.

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

Usage Guidelines3/5

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

The description explains to pass entity fields in camelCase and notes backend validation, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., update_workflow for modifications).

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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