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secret_create

Create a Docker swarm secret to store sensitive data securely; payloads cannot be read back or changed after creation.

Instructions

Create a swarm secret; requires a swarm manager.

Write-once: the payload can never be read back through the API (secret_inspect returns metadata only) and cannot be changed later - to rotate, create a new secret and update the consuming services, keeping your own copy of the value. For non-sensitive data that should stay readable, use config_create instead. Created secrets are stamped with provenance labels.

Args: name: Name for the secret (unique within the swarm) data: The secret payload (max 500 KB; must be empty when driver is set) labels: Labels to set on the secret driver: Secret-driver config for values held in an external store

Returns: dict: The created secret's full document (ID and Spec metadata; never the payload)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
nameYes
driverNo
labelsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.0

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnly=false and destructive=false annotations, the description discloses irreversible write-once behavior, the inability to read the payload back through the API, provenance-label stamping, and the data/driver interdependency. This is substantial behavioral context with no contradiction against the annotations.

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 front-loaded with the core action and prerequisite, followed by a tight behavioral paragraph and a scannable Args/Returns block. Every sentence adds necessary information; there is no filler or redundancy.

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?

With no output schema and no parameter descriptions in the schema, the description still covers the return document (ID and metadata, never payload), the swarm-manager prerequisite, rotation workflow, the alternative tool, and all parameter constraints. An agent has everything required to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the Args section compensates fully by documenting all four parameters with meaningful constraints: uniqueness of name, 500 KB payload limit, the requirement that data be empty when driver is set, and the external-store semantics of driver. This adds critical meaning the bare schema lacks.

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 first sentence, 'Create a swarm secret', names a specific verb and resource, matching the tool name exactly. It also explicitly contrasts with `config_create`, a close sibling, so an agent can distinguish the two without opening schemas.

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

Usage Guidelines5/5

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

The description states a hard prerequisite ('requires a swarm manager'), explains the write-once consumption pattern, and explicitly directs users to `config_create` for non-sensitive readable data. This gives clear when-to-use and when-not-to-use guidance.

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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