Skip to main content
Glama

check_new_mails

Check all connected mail accounts for unread emails since the last check. Get a summary with folder/category counts, urgent items, spam alerts, and connection errors.

Instructions

[K-Mail-MCP 전용] 등록된 전체 메일 계정에서 마지막 확인 이후 읽지 않은 메일을 수집합니다. v1.3.0: 매 실행마다 IMAP 폴더 변경을 자동 감지합니다. 신규 폴더는 누적 로그(discovered)에 추가됩니다. 100개 초과 시 오래된 것부터 밀어냅니다. 폴더 삭제 시그널(IMAP 목록에서 사라지거나 NONEXISTENT 오류)이 있으면 자동 제거합니다. 메일이 없는 폴더는 제거하지 않습니다. 폴더 제외 관리는 set_watched_mailboxes 툴을 사용하세요. reply_to_differs=true인 메일은 반드시 ⚠️ 표시하세요. 출력 형식: 1) 📋 요약 — 총 N통, 폴더별/카테고리별 건수 2) 🔴 즉시 확인 필요 3) 📧 전체 목록 — [계정/폴더] 발신자 — 제목 | 카테고리 | 한줄요약 | 링크 4) ⚠️ 스팸 의심 (isSpam=true만, 없으면 생략) 5) 🔴 연결 오류 (있을 때만)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_labelNoall
override_sinceNo
max_per_accountNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.5

TDQS

A3.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers extensively: it discloses automatic IMAP folder change detection, new folder logging, the 100-item eviction policy, deletion-signal handling, non-removal of empty folders, required ⚠️ marking for reply_to_differs=true, and the full output structure. This goes well beyond minimal viability.

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 long but front-loaded with the core purpose, followed by behavior and output format. Each section earns its place, though the density and lack of bullet formatting make it slightly harder to scan than ideal.

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 output format is fully described and folder-related behavior is very complete, which is helpful given there is no output schema. However, the three parameters are completely undocumented, so the description is not fully sufficient for an agent to confidently call the tool with non-default arguments.

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

Parameters1/5

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

Schema description coverage is 0% and the description never explains account_label, override_since, or max_per_account. Even the 100-item mention refers to an internal eviction rule, not to the max_per_account parameter. The agent is left to guess the format and semantics of override_since and the selection behavior of account_label.

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 '수집합니다' and a clear resource: unread mails from all registered accounts since the last check. It also clearly scopes the tool to K-Mail-MCP and differentiates it from siblings like read_email by emphasizing the batch collection over registered accounts.

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 explicitly directs users to set_watched_mailboxes for folder exclusion management, which is a useful alternative-tool pointer. It does not explicitly contrast check_new_mails with read_email or list_accounts, but the batch-collection purpose is clear enough to infer when to use it.

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