assetcache-mcp

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suggest_packs

Suggests ranked asset pack candidates matching a natural language query, giving users the ability to choose the most suitable pack.

Instructions

자연어 쿼리에 어울리는 팩 후보를 정렬한다 (사용자에게 팩 선택권 제공).

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": {
      "req": "<req>"
    },
    "name": "suggest_packs"
  }
}

Input Schema

Name	Required	Description	Default
`req`	Yes

Output Schema

Name	Required	Description	Default
`packs`	Yes
`query_id`	Yes
`project_context`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.7Aug 1, 2026

TDQS

B3.3/5.0

Behavior2/5

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

No annotations exist, so the description carries the full burden of behavioral disclosure. It only states sorting and user choice, with no mention of side effects, permissions, or ordering criteria. The tool appears read-only, but this is not explicit.

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 one concise, front-loaded sentence with no filler. The purpose is stated immediately, and the parenthetical adds relevant user-choice context.

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

Completeness2/5

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

With no annotations, an output schema, and several parameters, the one-sentence description is insufficient. It does not explain key parameters or usage nuances, making it hard for an agent to construct a valid req or know when to choose this tool over siblings.

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

Parameters2/5

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

Schema description coverage is 0% and the description does not explain any of the seven nested fields (kind, count, query, project_id, include_samples, include_thumbnails, min_matching_assets). Defaults and enums in the schema help, but the description adds no value beyond the tool name.

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 uses a specific verb '정렬한다' (sorts/ranks) and resource '팩 후보' (pack candidates), clearly distinguishing it from siblings like list_packs. It also adds the purpose of aligning with natural language queries and providing user choice.

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 implied use case is when a natural language query needs ranked pack candidates, but no explicit when-to-use or alternative tool exclusions are provided. Sibling tools like list_packs and request_user_pick are not referenced.

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