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create_service

Create a Docker Swarm service for replicated or global scheduling, rolling updates, and automatic restart across the swarm.

Instructions

Create a Swarm service; requires a swarm manager node.

Use this instead of run_container when you need replicated or global scheduling, rolling updates, or automatic restart across the swarm. Common extra_kwargs keys: name (str), env (list of "KEY=VAL"), mode ({"Replicated": {"Replicas": N}} or {"Global": {}}), networks (list of network names/ids), endpoint_spec ({"Ports": [{"PublishedPort": 80, "TargetPort": 8080}]}), labels (dict), restart_policy ({"Condition": "on-failure", "MaxAttempts": 3}), resources ({"Limits": {"NanoCPUs": 500000000, "MemoryBytes": 134217728}}).

args: image - Image to run service tasks from (e.g. "nginx:alpine") command - Override the image's default command; string or list of strings extra_kwargs - Additional docker-py ServiceCollection.create keyword arguments returns: dict - The created service's attrs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes
commandNo
extra_kwargsNo
Behavior5/5

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

Annotations (readOnlyHint=false, destructiveHint=false) indicate a non-destructive write operation. The description adds behavioral context: requires swarm manager, returns dict of created service attrs, and lists common extra_kwargs to show flexibility. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with front-loaded purpose and usage guidance, followed by parameter details. While somewhat lengthy, every sentence adds value; minor redundancy could be trimmed but overall effective.

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?

Given no output schema, description explains return as dict of service attrs. Covers all three parameters, prerequisites, alternative comparison, and common extra_kwargs. Sufficient for an agent to invoke 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 has 0% description coverage, but the description thoroughly explains all parameters: 'image' with example, 'command' with type explanation, and 'extra_kwargs' with enumerated common keys and examples. This compensates well beyond the bare schema.

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 'Create a Swarm service' with a specific verb and resource. It distinguishes from sibling tool `run_container` by listing use cases for Swarm features like replicated/global scheduling, rolling updates, and automatic restart.

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?

Explicitly provides when to use this tool over alternatives: 'Use this instead of `run_container` when you need...' Also mentions prerequisite 'requires a swarm manager node', giving clear usage context.

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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