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AxonityAI

Axonity Flow MCP Server

Official
by AxonityAI

create_prompt_snippet

Create a new prompt snippet draft by providing its fields in camelCase format. The backend validates the input and returns the created snippet with its unique ID and version.

Instructions

Create a new prompt_snippet draft. Pass the entity's fields (camelCase) in fields; the backend validates them. Returns the created prompt_snippet with its id and version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYesThe prompt_snippet's fields as a JSON object (camelCase keys), e.g. { "name": "…", "description": "…" }.
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 conveys that the tool creates a draft and performs backend validation, and returns the object with id/version. However, it omits potential prerequisites, authentication needs, or any side effects, leaving some behavioral gaps.

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, no wasted words. The first sentence states the core action, and the second adds essential details about parameter usage and return value. Efficient and well-structured.

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 one parameter with nested objects, no output schema, and no annotations, the description adequately explains the parameter, validation, and return value. It does not list all possible fields, but that is acceptable for a create tool where fields are defined elsewhere. It is generally complete for its complexity.

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 schema already describes the 'fields' parameter as an object and provides an example. The description adds meaning by specifying camelCase keys and stating the backend validates them, which goes beyond the schema's minimal 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 creates a new prompt_snippet draft, specifying it's a draft and that it returns the created snippet with id and version. This distinguishes it from related tools (update, delete, read) and provides a specific verb-resource combination.

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 implies use for creating a new prompt snippet (draft), but does not explicitly state when to use this tool versus alternatives like update or other snippet-related operations. No when-not-to-use or alternative guidance is provided.

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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