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by app.apick

LLM 모델 카탈로그

llm_models
Read-only

List available text-generation LLM models with per-token pricing and max context. 텍스트 생성 모델 카탈로그를 반환합니다. 각 모델의 1M 토큰당 input/output 단가(포인트), 계열·크기·멀티모달 여부·태그·추천 용도(use_cases)·max_context 를 한 응답에 포함합니다. llm_chat Tool의 model 입력값을 찾을 때 사용합니다. 무료입니다. [무료]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNo특수 태그 필터 — 'reasoning'(추론 특화) | 'coder'(코딩 특화)
familyNo모델 계열 필터 (deepseek, qwen, glm, google, nvidia, llama, mistral, gpt-oss, moonshot, seed, mimo, phi)
use_caseNo추천 용도 필터 — 'general' | 'reasoning' | 'coding' | 'multimodal' | 'economy'
multimodalNo멀티모달(이미지 이해) 지원 여부 필터 (true/false)

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, and the description adds valuable context about response contents (pricing per 1M tokens, family, size, multimodality, tags, use_cases, max_context) and notes the tool is free. This goes beyond the structured annotations, though it does not discuss potential response size or pagination.

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 mostly concise and front-loaded with the main purpose. However, it contains redundancy: the same information is repeated in English and Korean, and '무료입니다' appears alongside '[무료]', which slightly wastes space. Still, it remains an efficient single-paragraph description.

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?

With no output schema, the description takes responsibility for explaining return values, which it does by listing the included fields. It also provides the integration context with llm_chat and the free status. For a 4-parameter filter tool, this is sufficiently complete, though it could mention that all filters are optional.

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?

All 4 parameters have complete descriptions in the schema (100% coverage), so baseline is 3. The description does not repeat parameter details but does contextualize the tool's purpose for model selection. No additional parameter-level meaning is added beyond the schema.

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 lists available text-generation LLM models, specifying key attributes like pricing and context length. It also explicitly ties the tool to finding model input values for llm_chat, distinguishing it from sibling tools.

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?

Provides explicit guidance on when to use (finding model input values for llm_chat), but does not mention alternatives or exclusions. The context is clear, so it earns a 4 rather than 5.

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes; even within families like identi_card1-5 vs identi_card_image1-5, the text-input vs image-input distinction is clear. However, the sheer number of tools and some near-synonyms (e.g., ocr_identi1 vs identi_card_image1) could cause occasional misselection, but descriptions mitigate this.

Naming Consistency3/5

Naming follows a loose verb-first pattern (check_, crawl_, download_, draw_, etc.) but includes significant deviations: bare nouns (bank_code, location, whois), numbered variants (identi_card1, identi_card_image1), and mixed prefixes (ocr_, identity_, etc.). The inconsistency is noticeable but still readable and predictable within functional clusters.

Tool Count3/5

80 tools is far above the typical 3-15, but the server is a broad API aggregator covering many independent domains (banking, ID verification, media conversion, search, LLM, etc.), so the high count is somewhat justified. Still, the sheer number makes the toolkit feel unwieldy and hard to navigate, placing it at the high end of acceptable.

Completeness4/5

Within its stated purpose as a general-purpose utility API, the toolset covers a wide array of common task families: identity document verification (text and image), OCR field extraction, media conversion, web/search, domain/IP lookup, and LLM chat. Most operations have both get and act variants (e.g., set/get watermark, parcel_tracking/auto), with few obvious dead ends for typical use cases.

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