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aceternity-mcp

aceternity-mcp

by dvnold
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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match_components_to_project

Match components to your project description to plan layouts. Returns layout-critical components for full project planning.

Instructions

Match components to a whole project description.

Similar to recommend_components but biased toward layout-critical components and returns more results for full project planning.

Args: project_description: Project description (e.g. "dashboard with dark mode") max_results: Number of results (default 15, max 100) include_pro: Include pro components (default True)

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": {
      "project_description": "<project_description>"
    },
    "name": "match_components_to_project"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_descriptionYes
max_resultsNo
include_proNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.4.1

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so description carries full burden. It discloses that the tool returns more results and is biased, but does not explicitly state whether it is read-only or has side effects. For a matching tool, safety profile (e.g., no modification) is implied but not stated, leaving some ambiguity.

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?

Description is very concise: two sentences for purpose/context, then clean bullet-style args list. No wasted words, front-loaded with key differentiation.

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 that output schema exists (but not shown here), description does not need to explain return structure. It covers purpose, bias, and parameters well. Could be improved by noting that the tool is read-only or clarifying 'layout-critical,' but overall adequate.

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 coverage is 0% (no descriptions in schema), so description must add meaning. It does so clearly: gives example for project_description, specifies default and max for max_results, and default for include_pro. These details go beyond the schema's type/default information.

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?

Description clearly states the tool matches components to a project description. It explicitly distinguishes from the sibling 'recommend_components' by noting bias toward layout-critical components and returning more results for full project planning. Verb 'match' and resource are specific.

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?

Description provides clear context: use for full project planning with bias toward layout-critical components. It contrasts with recommend_components via 'similar to' but does not explicitly state when NOT to use. However, the context is sufficient for an agent to decide.

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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