dead-letter
dead-letter
.eml 파일에 두 번째 생명을 주세요.
dead-letter는 이메일 내보내기를 YAML front matter가 포함된 깔끔한 Markdown으로 변환합니다. 스레드는 분리되고, 서명은 제거되며, 첨부 파일은 추출되고, 캘린더는 파싱됩니다. 파일 하나든 만 개든 상관없습니다.
✨ 기능
완전 충실도 변환 — HTML 정화, Gmail/Outlook 스레드 분할, 인라인 이미지 처리, 캘린더 이벤트 요약
CLI — 파일이나 디렉터리를 지정하기만 하면 됩니다
로컬 웹 UI — 드래그 앤 드롭 가져오기, watch 모드, 변환 등급 배지, 처리 기록, 작업별 진단을 제공하는 다크 커맨드 센터 인터페이스
Inbox/Cabinet 워크플로 —
.eml파일을 Inbox에 넣으면 dead-letter가 Markdown 번들을 Cabinet으로 정리합니다설치 검증 —
dead-letter doctor가 런타임 환경을 확인합니다변환 보고서 — 자동화 및 감사를 위한 첨부 파일 참조/보존 개수를 포함한 파일별 진단이 담긴 옵트인 JSON 보고서
MCP 서버 — Claude Desktop, Claude Code, Codex 및 기타 MCP 클라이언트와 통합
Claude 플러그인 — Claude Code 또는 Cowork에서 네 개의 슬래시 명령(
/dead-letter:convert,/dead-letter:summarize,/dead-letter:triage,/dead-letter:cabinet)으로 원커맨드 설치Python API —
from dead_letter import convert한 줄이면 시작됩니다
Related MCP server: DingusMail
🧠 LLM 파이프라인을 위해 설계
원시 .eml 파일은 다운스트림 LLM 및 검색 파이프라인에 노이즈가 많은 입력입니다. MIME 헤더, multipart 경계, 중복된 HTML/plain 본문, 인코딩된 첨부 파일이 모두 텍스트 경로에 섞여 들어갑니다.
dead-letter는 이를 YAML front matter가 포함된 Markdown으로 정규화하므로 MIME 파싱이나 base64 정리 없이 메시지 텍스트와 메타데이터를 청킹 또는 인덱싱에 바로 사용할 수 있습니다. 기본 convert() 및 convert_dir() 실행은 메시지당 하나의 .md 파일을 작성하고 첨부 파일 이름을 front matter에 유지합니다.
파일 시스템 아티팩트도 분리하려면 bundle 및 Cabinet 워크플로가 message.md와 보존된 디코딩 파일을 attachments/ 아래에 작성합니다. Markdown은 텍스트 수집에 바로 사용할 수 있고, PDF, 스프레드시트, 캘린더 파일 및 기타 보존된 바이너리 첨부 파일은 이미 사용 중인 다운스트림 파서를 위해 깔끔하게 분리된 상태로 유지됩니다.
직접적인 LLM 통합을 위해 MCP 서버를 사용하면 Claude Desktop, Claude Code, Codex 및 기타 MCP 클라이언트가 셸 명령 없이 dead-letter의 변환 도구를 호출할 수 있습니다.
📊 토큰 비용 벤치마크
dead-letter의 가치는 모든 대안보다 토큰이 적다는 것이 아니라 토큰당 충실도(fidelity per token), 즉 이메일을 온전하게 유지하는 가장 저렴한 표현입니다. HTML 스레드, 첨부 파일, 뉴스레터로 구성된 합성 코퍼스에서 측정했습니다(토크나이저 o200k_base, 중앙값):
원시
.eml보다 토큰 약 88% 감소 — PDF 첨부 파일이 있는 이메일 하나는 원시 기준 약 126k 토큰인 반면 변환 시 약 180 토큰입니다.이메일을 온전하게 유지하는 유일한 표현 — 스레드 구조, 메시지별 발신자 표시, 링크, 첨부 파일 메타데이터가 모두 보존됩니다. 단순 텍스트 추출이 더 저렴한 이유는 정확히 이러한 요소를 버리기 때문입니다(첨부 파일 0/2 보존 vs dead-letter의 2/2).
벤치마크는 어디에서 손해를 보는지 정직하게 공개합니다. 첨부 파일, 링크, 스레드 구조를 버려도 괜찮다면 단순 추출이 토큰이 더 적습니다. 전체 방법, 완전한 표(해당 행 포함), 토크나이저 공개, 원커맨드 재현 방법은 benchmarks/에 있습니다.
📦 설치
Apple silicon macOS에서 Homebrew로 설치:
brew tap BigCactusLabs/tap
brew install dead-letterHomebrew 포뮬러는 핵심 CLI만 설치합니다: dead-letter convert 및 dead-letter doctor. 선택적 웹 UI나 MCP 서버 의존성 스택은 의도적으로 번들하지 않습니다.
pip로 설치:
pip install dead-letter # core + CLI
pip install dead-letter[cli] # + watchfiles (used by backend/UI watch mode)
pip install dead-letter[ui] # + web UI, API server, and watch mode
pip install dead-letter[mcp] # + MCP serverUI 또는 MCP를 격리된 환경에 설치하려면 pipx를 사용하세요:
pipx install 'dead-letter[ui]' # installs dead-letter and dead-letter-ui
pipx install 'dead-letter[mcp]' # installs dead-letter and dead-letter-mcp소스에서 설치:
git clone https://github.com/BigCactusLabs/dead-letter.git
cd dead-letter
uv sync --extra dev # all extras
uv sync --extra ui # UI only
uv sync --extra mcp # MCP only🚀 빠른 시작
CLI — 단일 파일 변환:
dead-letter convert message.eml전체 디렉터리 변환:
dead-letter convert inbox/ --output out/출력과 함께 JSON 변환 보고서 생성:
dead-letter convert inbox/ --output out/ --report--output을 지정하면 보고서는 해당 출력 디렉터리에 .dead-letter-report.json으로 작성됩니다. --output이 없으면 파일 변환은 소스 메시지 옆에, 디렉터리 변환은 입력 디렉터리 루트에 보고서를 작성합니다.
런타임 환경 확인:
dead-letter doctor디렉터리 변환은 .eml 파일을 재귀적으로 스캔하고, 접미사를 대소문자 구분 없이 매칭하며, 확인된 대상이 요청된 입력 트리를 벗어나는 심링크 파일을 건너뛰고, 동일한 메시지 파일로 확인되는 트리 내 심링크 별칭을 중복 제거합니다.
웹 UI — 로컬 서버 시작:
dead-letter-ui --host 127.0.0.1 --port 8765http://127.0.0.1:8765을 엽니다. 첫 실행 시 설정 프롬프트가 기본 Inbox 및 Cabinet 폴더를 제안합니다. 구성하거나 건너뛰고 변환을 시작하세요. 드래그 앤 드롭 또는 파일 선택기로 .eml 파일을 가져옵니다. 단일 파일 가져오기는 파일 모드를 사용하고, 다중 파일 드롭은 디렉터리 모드 배치 작업 하나를 생성합니다. 혼합 드롭은 .eml이 아닌 파일을 건너뛰기 전에 확인을 요청합니다. 백엔드는 단일 및 배치 업로드 모두에 파일당 100MB 가져오기 제한을 적용합니다.
소스 체크아웃에서는 uv run을 접두어로 붙이세요:
uv run dead-letter convert message.eml
uv run --extra ui dead-letter-ui --host 127.0.0.1 --port 8765🐍 Python API
from dead_letter import convert
result = convert("message.eml")
print(result.subject, result.sender)
print(result.output) # path to the generated .md옵션 사용:
from dead_letter import convert, ConvertOptions
result = convert("message.eml", options=ConvertOptions(
strip_signatures=True,
strip_quoted_headers=True,
))서명 이미지(로고, 소셜 아이콘) 및 추적 픽셀 제거:
result = convert("message.eml", options=ConvertOptions(
strip_signature_images=True,
strip_tracking_pixels=True,
))활성화하면 이러한 필터는 렌더링된 Markdown에서 일치하는 이미지를 제거하고, 제거된 인라인 서명/추적 자산을 bundle 첨부 파일 출력에서 생략합니다.
Bundle 변환(하나의 디렉터리에 Markdown + 첨부 파일 + 소스):
from dead_letter import convert_to_bundle
bundle = convert_to_bundle("message.eml", bundle_root="cabinet/", source_handling="copy")
print(bundle.markdown) # cabinet/message/message.md
print(bundle.attachments) # retained extracted files under cabinet/message/attachments/source_handling="copy"는 원본 .eml을 제자리에 보존합니다. 생략하면 convert_to_bundle()은 기본값인 source_handling="move"로 소스 메시지를 bundle로 이동합니다.
보존된 추출 첨부 파일 이름은 attachments/ 아래에 작성되기 전에 안전한 기본 이름으로 정규화됩니다.
품질 진단에는 보존 대상 첨부 파일이 있는 메시지의 참조/보존 첨부 파일 개수가 포함되므로, 누락된 아티팩트를 기계적으로 감지할 수 있습니다. 품질 진단을 참조하세요.
배치:
from dead_letter import convert_dir
for r in convert_dir("inbox/", output="out/"):
print(f"{'✓' if r.success else '✗'} {r.source.name}")🔌 MCP 서버
dead-letter는 LLM 클라이언트가 셸 명령 없이 .eml 파일을 직접 변환할 수 있도록 MCP 서버를 제공합니다.
설치 및 실행:
pip install dead-letter[mcp]
dead-letter-mcp소스 체크아웃에서:
uv run --extra mcp dead-letter-mcpClaude Desktop — claude_desktop_config.json에 추가:
{
"mcpServers": {
"dead-letter": {
"command": "uv",
"args": ["--directory", "/path/to/dead-letter", "run", "--extra", "mcp", "dead-letter-mcp"]
}
}
}Claude Code 또는 Cowork(권장 — Claude 플러그인):
/plugin marketplace add BigCactusLabs/bigcactuslabs-plugins
/plugin install dead-letter플러그인은 MCP 서버를 번들합니다(uvx를 통해, pip install 불필요 — PATH에 uv만 있으면 됩니다) 그리고 네 개의 슬래시 명령을 추가합니다: /dead-letter:convert, /dead-letter:summarize, /dead-letter:triage, /dead-letter:cabinet. 플러그인을 통해 처리되는 이메일 콘텐츠는 지침이 아닌 신뢰할 수 없는 데이터로 취급되므로, 메시지에 포함된 도구 사용, 자격 증명, 데이터 유출 요청은 수행되지 않습니다. 소스는 plugin/에 있습니다.
마켓플레이스는 게시된 각 플러그인 태그와 커밋을 고정합니다. 릴리스 자동화는 번들된 MCP 서버의 정확한 PyPI 버전이 라이브된 후에만 해당 포인터를 업데이트하므로, Claude Code와 Cowork는 동일한 재현 가능한 릴리스를 확인합니다.
Claude Code(수동 MCP 추가 — 대안):
claude mcp add dead-letter -- uv run --extra mcp dead-letter-mcpCodex:
codex mcp add dead-letter -- uv run --extra mcp dead-letter-mcp
codex mcp listcodex mcp add 명령은 로컬 dead-letter MCP 서버를 등록하고, codex mcp list는 사용 가능 여부를 확인합니다.
도구
도구 | 필수 인수 | 반환값 |
|
| Markdown 텍스트. |
|
|
|
|
| JSON 요약. 호출당 |
|
| 품질 및 구조 JSON. 영구적으로 기록되는 것은 없습니다. |
네 도구 모두 preset(default, clean, verbose, raw)과 플래그별 재정의를 받습니다. MCP 전용 제약 조건과 오류 텍스트 표를 포함한 전체 계약: docs/reference/v4-runtime-contracts.md.
🗂 프로젝트 구조
src/dead_letter/
├── core/ # conversion pipeline (MIME, HTML, threads, rendering)
├── backend/ # CLI, API server, job runner, watch mode, MCP server
└── frontend/ # static web UI (Alpine.js ES modules + vanilla fetch)
tests/
├── core/ # conversion pipeline tests with .eml fixtures
├── backend/ # API, job, and watch tests
├── plugin/ # Claude plugin manifest, skill, and command tests
└── frontend/ # JS unit tests🧪 테스트
uv run pytest -q tests/core # conversion pipeline
uv run pytest -q tests/backend # API and job runner
uv run pytest -q tests/plugin # Claude plugin manifest, skill, and command surfaces
node --test tests/frontend/*.test.js # frontendCI는 PR 및 main 또는 feat/** 브랜치에 대한 푸시에서 동일한 명령으로 네 가지를 모두 실행하며, 추가로 npx --yes @anthropic-ai/claude-code@2.1.145 plugin validate plugin/ 및 node --check src/dead_letter/frontend/static/app.js도 실행합니다.
📚 문서
🔧 우리가 사랑하는 도구
MarkEdit — Markdown용 TextEdit, 네이티브 macOS, 약 4MB. dead-letter 출력을 마치 원래 그곳에 있어야 했던 것처럼 엽니다.
mo — 라이브 리로드로 브라우저에서 파일을 렌더링하는 로컬 Markdown 뷰어. Cabinet을 가리키면 변환된 메일을 피드처럼 읽을 수 있습니다.
⚠️ 알려진 제한 사항
로컬 전용 — 원격 서버 없음, 인증 없음
인메모리 작업 레지스트리(재시작 시 상태 초기화)
단일 사용자, 단일 머신
라이선스
PolyForm Noncommercial 1.0.0 — 개인, 교육, 비영리 목적 사용은 무료입니다. 상업적 사용은 Big Cactus Labs로부터 별도 라이선스를 취득해야 합니다.
Available Tools
4 toolsconvert_directoryA
Batch convert all .eml files in a directory to Markdown.
Recursively finds all .eml files and converts them. Returns a JSON summary with total, successes, failures, output_paths, and errors.
Use convert_eml to retrieve individual converted file content.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | default | |
| dry_run | No | ||
| directory | Yes | ||
| thread_mode | No | latest | |
| thread_order | No | oldest-first | |
| include_raw_html | No | ||
| output_directory | No | ||
| strip_signatures | No | ||
| strip_disclaimers | No | ||
| embed_inline_images | No | ||
| include_all_headers | No | ||
| no_calendar_summary | No | ||
| strip_quoted_headers | No | ||
| strip_tracking_pixels | No | ||
| strip_signature_images | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description adds value by stating batch conversion, recursion, and JSON summary structure, but lacks info on side effects, permissions, or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, front-loaded with purpose, no fluff, and ends with a helpful alternative reference.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (15 parameters), the description covers only basic behavior and output, leaving the agent without insight into key configuration options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 15 parameters, the description provides no explanation for any parameter beyond the directory. Agent has no guidance on presets, dry_run, etc.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool batch converts .eml files in a directory to Markdown, with recursive behavior, and distinguishes itself from sibling convert_eml by mentioning individual file retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear alternative: use convert_eml for individual file content. However, it does not explicitly state when not to use this tool or mention the sibling convert_eml_to_bundle.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_emlA
Convert a .eml email file to Markdown with YAML front matter.
Returns the full Markdown content (front matter + body). When output_path is provided, also writes the file to disk.
Presets bundle common flag combinations:
default: strips signatures, tracking pixels, signature images
clean: default + strips disclaimers and quoted headers
verbose: includes all headers and raw HTML
raw: no stripping, preserves everything
Individual flags override the preset when provided.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | default | |
| eml_path | Yes | ||
| output_path | No | ||
| thread_mode | No | latest | |
| thread_order | No | oldest-first | |
| include_raw_html | No | ||
| strip_signatures | No | ||
| strip_disclaimers | No | ||
| embed_inline_images | No | ||
| include_all_headers | No | ||
| no_calendar_summary | No | ||
| strip_quoted_headers | No | ||
| strip_tracking_pixels | No | ||
| strip_signature_images | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the return value (Markdown with front matter), the optional disk write, and the behavior of presets and flag overrides. However, it does not explain the thread_mode and thread_order parameters, leaving some behavioral aspects unexplained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with six sentences, front-loaded with the core action, and uses a clear bullet-like list for presets. Every sentence adds value without repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (14 parameters, presets, output schema), the description covers the main purpose, return value, presets, and override logic. It lacks explanation for thread_mode and thread_order, but overall provides sufficient context for most use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning. It explains presets and mentions several flags (signatures, tracking pixels, etc.), and notes that individual flags override presets. However, it omits details for thread_mode, thread_order, and some boolean flags. The presets bundling compensates partially.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts .eml to Markdown with YAML front matter, specifying the output format and the optional file write. It implicitly distinguishes from siblings like convert_directory and convert_eml_to_bundle by focusing on a single file conversion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on preset usage and flag overrides, but it does not explicitly state when to use this tool versus sibling tools like convert_directory or convert_eml_to_bundle, which would help an agent choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_eml_to_bundleB
Convert a .eml file to a self-contained bundle with markdown and attachments.
Creates a directory containing the converted markdown, extracted attachments, and optionally the original .eml source.
source_handling only accepts 'copy' over MCP: the original .eml is copied into the bundle and left untouched. The 'move' and 'delete' modes are rejected here — use the CLI or the Python API for those.
Returns JSON with bundle_path, markdown_path, attachment_paths, and optional diagnostics.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | default | |
| eml_path | Yes | ||
| bundle_root | Yes | ||
| thread_mode | No | latest | |
| thread_order | No | oldest-first | |
| source_handling | No | copy | |
| include_raw_html | No | ||
| strip_signatures | No | ||
| strip_disclaimers | No | ||
| embed_inline_images | No | ||
| include_all_headers | No | ||
| no_calendar_summary | No | ||
| strip_quoted_headers | No | ||
| strip_tracking_pixels | No | ||
| strip_signature_images | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool creates a directory, copies the .eml, leaves the source untouched, and returns a JSON structure with specific fields. It also discloses that move/delete modes are rejected. However, it does not mention potential side effects like overwriting existing directories, error handling, or permissions. Still, the core mutation and side-effect profile is clear, warranting a 4.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is about 120 words, organized into a brief purpose statement, a note on source_handling, and a return-value summary. It is not excessively verbose and front-loads the core action. Some redundancy exists (e.g., stating the return format), but it remains appropriately sized for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite an output schema (which the description partially covers by naming returned fields), the tool has 15 parameters and 0% schema description coverage. The description only addresses source_handling, leaving the meaning of presets, thread modes, and all boolean flags unexplained. This is a significant gap for an agent to invoke the tool correctly with the full range of options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only explains source_handling, noting the 'copy' limitation. The other 14 parameters (preset, thread_mode, thread_order, boolean flags) are left undefined. The description adds value for one parameter but fails to clarify the vast majority, leaving agents without essential meaning for the options they may set.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: converting an .eml file into a self-contained bundle with markdown and attachments. It uses a specific verb and resource, but does not differentiate from sibling tools like convert_eml or convert_directory. The purpose is unambiguous, earning a 4 rather than a 5 because it lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a constraint on source_handling (only 'copy' is accepted over MCP, with guidance to use CLI/API for other modes) but does not explain when to choose this tool over its siblings. There is no mention of convert_eml, convert_directory, or get_diagnostics as alternatives for different scenarios. The guidance is parameter-specific rather than tool-selection-focused.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_diagnosticsA
Inspect email quality and structure without writing permanent files.
Use this to assess conversion quality before committing, or to troubleshoot problematic .eml files.
Always returns JSON with: state (normal/degraded/review_recommended), selected_body, segmentation_path, client_hint, confidence, fallback_used, and warnings. Two keys are conditional: stripped_images appears only when images were removed, and attachments only when the message had attachments eligible for retention.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | No | default | |
| eml_path | Yes | ||
| thread_mode | No | latest | |
| thread_order | No | oldest-first | |
| include_raw_html | No | ||
| strip_signatures | No | ||
| strip_disclaimers | No | ||
| embed_inline_images | No | ||
| include_all_headers | No | ||
| no_calendar_summary | No | ||
| strip_quoted_headers | No | ||
| strip_tracking_pixels | No | ||
| strip_signature_images | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It explicitly states the operation is non-destructive ('without writing permanent files'), and thoroughly describes the return structure: 'Always returns JSON with: state (normal/degraded/review_recommended), selected_body, segmentation_path, client_hint, confidence, fallback_used, and warnings.' It also details conditional keys (stripped_images only when images removed, attachments only when eligible), providing comprehensive insight into output behavior without relying on annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise: it opens with the core purpose, then provides usage guidance, and ends with a precise list of return keys and conditional behaviors. Each sentence adds value, there is no fluff, and the most critical information (non-destructive, purpose, use cases) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description thoroughly explains the return format and gives usage context, it leaves the 13 parameters completely undocumented. Given the tool's complexity (multiple enums, boolean toggles) and the lack of schema descriptions, an agent would not be able to correctly configure parameters without external knowledge. The output schema exists (per context signals) and the description explains return values, but the absence of parameter semantics makes the definition incomplete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description provides no explanation of any of the 13 parameters. While the description mentions some related behaviors (e.g., conditional keys for stripped images and attachments), it does not explain what parameters like 'preset', 'thread_mode', 'strip_signatures', or 'include_raw_html' actually control. The agent is left to infer from parameter names alone, which is insufficient for a tool with this many options. The description fails to compensate for the schema's lack of parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Inspect email quality and structure without writing permanent files.' It specifies the verb ('inspect'), the resource ('email quality and structure'), and the non-destructive nature. It also names use cases ('assess conversion quality before committing, or to troubleshoot problematic .eml files'), which effectively distinguishes it from the sibling conversion tools (convert_eml, convert_eml_to_bundle, convert_directory) that perform transformations rather than inspection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage scenarios: 'Use this to assess conversion quality before committing, or to troubleshoot problematic .eml files.' This gives clear context for when to use the tool. However, it does not explicitly state when not to use it or mention the sibling conversion tools as alternatives, relying on the implicit inference that conversion tools are for transforming files while this inspects them. A slight improvement would be naming the alternatives directly, so 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
4 tool updates
v0.2.4- First observed
convert_directory - First observed
convert_eml - First observed
convert_eml_to_bundle - First observed
get_diagnostics
TDQS
The tools have distinct purposes: batch conversion, single conversion, bundle conversion, and diagnostics. However, convert_eml and convert_eml_to_both overlap as single-file converters, though descriptions clarify the difference in output. No tools are truly ambiguous.
All conversion tools follow a consistent 'convert_' prefix, while get_diagnostics uses 'get_'. The pattern is clear and logical for each tool's function, with only one deviation that is still fitting.
With 4 tools, the server is well-scoped for an email conversion utility. Each tool serves a distinct and necessary function without redundancy or bloat.
The set covers batch conversion, single conversion, bundle creation, and diagnostics. A possible gap is the lack of a tool to manage or list existing bundles, but the core conversion workflow is complete.
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