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AI Canvas

AI Canvas

by aicanvas-me
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Get the AI Canvas install command for a component

get_install_command
Read-onlyIdempotent

Returns the exact shadcn CLI command to install a component, enabling direct installation when the project uses shadcn-aware setup; otherwise it falls back to manual component file writing.

Instructions

Return the shadcn CLI command to install a single component into the user's project. After calling this, suggest the user run the command (or run it yourself if you have shell access). Requires shadcn-aware setup (Tailwind v4, components.json) — for projects without that, fall back to get_component and write the file manually.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "get_install_command"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesThe component slug, e.g. "ai-job-cards".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent hints. The description adds valuable context by mentioning the prerequisite setup and the recommended post-call action. However, it does not detail the exact return format (e.g., plain string vs. JSON), which would be helpful without an output schema. No contradiction with 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 extremely concise, using two sentences to convey purpose, usage instructions, prerequisites, and a fallback. It is front-loaded with the core function and every sentence earns its place.

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?

For a tool with a single parameter, clear annotations, and no output schema, the description covers the essential aspects: what it does, when to use it, prerequisites, and what to do after calling. It is complete enough for an agent to invoke correctly without needing extra context.

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?

The input schema has 100% coverage, with slug described and an example provided. The description adds minimal parameter-specific meaning beyond that, but it reinforces that the command is for a single component. This is baseline-appropriate since the schema already handles parameter semantics.

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 returns a shadcn CLI command to install a single component, using a specific verb (return) and resource. It distinguishes itself from sibling tools like get_component by focusing on the install command rather than the component code itself.

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?

Explicit guidance is provided: it requires shadcn-aware setup (Tailwind v4, components.json) and specifies a fallback to get_component for projects without that setup. It also instructs to suggest running the command or run it yourself if shell access is available, giving clear when-to-use and alternative 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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