Skip to main content
Glama

ateam_create_plugin

Scaffold a UI plugin inside an existing connector by eliminating boilerplate code for imports, hooks, and postMessage protocol. Supports iframe, React Native, or both with automatic discovery on deploy.

Instructions

Scaffold a UI plugin (iframe HTML, React Native TSX, or both) inside an existing connector. Eliminates ~50% of identical plugin boilerplate (imports, theme/bridge hooks, postMessage protocol, default export shape). You then fill in the component body. Use kind='iframe' for web-only, 'rn' for mobile-only, 'adaptive' for both. Auto-discovery (Phase 5 of the strip) picks up the new plugin at next deploy without a manifest declaration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
solution_idYesThe solution ID
connector_idYesExisting connector to add the plugin into (e.g. 'personal-assistant-ui-mcp')
plugin_nameYesPlugin name (lowercase-with-dashes). E.g. 'memories-panel'. Becomes the dir name.
kindNoRender mode. 'adaptive' (default) produces both iframe + RN scaffolds.
Behavior3/5

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

With no annotations, the description carries full burden. It explains the tool scaffolds plugins and auto-discovers at next deploy, but does not disclose potential side effects, authentication needs, or rate limits. It adds some behavioral context but could be more comprehensive.

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?

The description is concise with four sentences, front-loaded with purpose. It avoids fluff, though the reference to 'Phase 5 of the strip' may be unclear to new users.

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 no output schema and no annotations, the description covers the essential aspects: what it does, parameter usage, and post-action behavior (auto-discovery). It is adequate for a scaffolding tool, though return values are not mentioned.

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?

Although schema coverage is 100%, the description adds value by explaining the kind enum values, giving an example for connector_id, and noting plugin_name becomes the directory name. This goes beyond the schema definitions.

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 scaffolds a UI plugin inside an existing connector, specifying types (iframe, rn, adaptive) and the benefit of eliminating boilerplate. It distinguishes from siblings like ateam_create_connector by focusing on plugin creation.

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

Usage Guidelines4/5

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

It provides guidance on when to use each kind value (iframe for web, rn for mobile, adaptive for both) and implies the prerequisite of an existing connector. However, it does not explicitly mention when not to use this tool or suggest alternative tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ariekogan/ateam-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server