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keviocastro

Paperclip AI Adapter for Antigravity CLI

by keviocastro

paperclip_create_agent

Create a new AI agent with a specific role in Paperclip, defining skills, team, runtime, and reporting to build your AI workforce.

Instructions

Create a new AI agent (hire) in the Paperclip organization with a specific role

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the agent
roleYesRole/title of the agent (e.g. Engineer, Designer, QA)
skillsNoList of skills for the agent
teamIdNoTeam ID to assign the agent to
runtimeNoExecution runtime (claude-code, codex, cursor, shell, http)
reportsToNoAgent ID this agent reports to in the org chart
descriptionNoDescription of the agent responsibilities

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It transparently identifies the action as creating/hiring a new agent, but it does not disclose behavioral details such as required permissions, whether creation is reversible, or what the response contains. The core side effect (creating a resource) is clear, so this is adequate but not rich.

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?

A single sentence conveys the action, resource, scope, and role without any filler. The key information is front-loaded and there is no redundant restatement of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The schema fully documents parameters and there are no nested objects, so invocation details are covered. However, the absence of annotations and an output schema leaves return-value expectations and operational caveats unaddressed; a more complete description would note what the caller receives or any administrative implications.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema already documents all seven parameters. The description only echoes the 'role' qualifier and does not add meaningful semantics beyond the schema, matching the baseline of 3.

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 states a specific verb ('Create'), a precise resource ('AI agent'), an organizational scope ('Paperclip organization'), and a distinguishing qualifier ('with a specific role'). This clearly separates it from sibling creation tools such as paperclip_create_issue or paperclip_create_project.

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 this tool is for hiring/creating a new agent, but it does not explicitly state when to prefer it over alternatives (e.g., paperclip_update_agent for existing agents) or any prerequisites/exclusions. The usage context is inferable from the name and siblings, not spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.