Skip to main content
Glama

글 표지 만들기

velog_render_cover

Create 1200x630 cover images for Velog posts and social previews. Automatically wraps long titles and adjusts font size to fit, then returns a URL for use as the post thumbnail.

Instructions

글 목록·SNS 미리보기에 쓸 표지 이미지(1200×630)를 만든다. 제목이 길면 줄바꿈하고, 그래도 안 들어가면 글자 크기를 줄인다 — 전부 실측 기준이다. 만든 뒤 velog_update_post 의 thumbnail 에 돌려받은 주소를 넣으면 표지가 된다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
toneNo
titleYes
footerNo우상단 서명 (예: '@milcho0604')
kickerNo상단 작은 라벨 (예: '디버깅 기록')
uploadNo
post_idNo
subtitleNo
Behavior4/5

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

Beyond the annotations (which are all false and provide no behavioral hints), the description discloses adaptive text fitting: line wrapping and font-size reduction based on actual measurements. It also implies the output is a URL ('returned address'). This adds meaningful context about how the tool behaves. It does not mention the upload default behavior, but no contradiction exists.

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 two sentences, front-loaded with the primary purpose, followed by behavior and integration instructions. Every clause provides value, with no redundancy or filler. It is concise and well-structured for agent consumption.

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

Completeness3/5

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

The description covers the core purpose, adaptive behavior, and integration with velog_update_post, which is essential for basic use. However, with eight parameters, no output schema, and sparse annotations, it omits details on several parameters (e.g., tone, subtitle, upload, post_id) and the exact return format. It is sufficient for a simple call but not fully complete for all scenarios.

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 only 25% (footer and kicker have descriptions). The description adds no semantic meaning for the remaining six parameters (title, subtitle, tone, tags, upload, post_id). It references title only in the context of wrapping behavior, not its semantics. Given the low schema coverage, the description fails to compensate, leaving agents without guidance for most parameters.

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's function: creates a cover image (1200×630) for post lists and SNS previews. It uses a specific verb (makes) and identifies the resource (cover image), effectively distinguishing it from sibling tools like velog_render_diagram and velog_upload_image.

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 clear context for when to use: to generate cover images for post lists/SNS previews. It also explicitly instructs to pass the returned URL to velog_update_post's thumbnail, demonstrating a concrete workflow. It doesn't state exclusions but offers sufficient guidance for correct usage.

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/milcho0604/velog-mcp'

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