Skip to main content
Glama

velog_publish_post

Publish drafted content to Velog by converting draft IDs into published posts, with optional privacy settings.

Instructions

초안(draft_id)을 Velog에 발행합니다. velog_draft_post 호출 후 사용하세요.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draft_idYesvelog_draft_post에서 반환된 draft_id
is_privateNo비공개 발행 여부 (초안 설정 덮어씀)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions that is_private parameter '덮어씀' (overwrites) draft settings, it doesn't describe what publishing entails (e.g., makes content publicly visible, triggers notifications), potential side effects, authentication requirements, or error conditions. For a mutation tool with zero annotation coverage, this is insufficient.

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 with just two sentences in Korean. The first sentence states the core purpose, and the second provides crucial usage guidance. Every word earns its place with zero wasted text, making it front-loaded and efficient.

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?

Given this is a mutation tool with no annotations and no output schema, the description should do more to explain behavioral aspects. While it covers basic purpose and workflow prerequisites adequately, it lacks information about what publishing actually does, potential consequences, or what the tool returns. The completeness is minimal but not entirely inadequate.

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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema - it only reinforces that draft_id comes from velog_draft_post (already in schema) and mentions that is_private overwrites draft settings (slightly helpful context). Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('발행합니다' - publishes) and the resource ('초안' - draft), specifying it's for Velog. It distinguishes from siblings like velog_draft_post by indicating this tool publishes rather than creates drafts. However, it doesn't explicitly differentiate from velog_update_post which might also involve publishing changes.

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?

The description provides clear context about when to use this tool ('velog_draft_post 호출 후 사용하세요' - use after calling velog_draft_post), establishing a prerequisite workflow. It doesn't explicitly state when NOT to use it or name alternatives, but the sibling relationship is implied through the naming convention.

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/seongwon030/velog_mcp'

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