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dead-letter

dead-letter

dead-letter

PyPI package Python versions License: PolyForm Noncommercial

あなたの .eml ファイルには、セカンドライフが必要です。

dead-letter は、メールのエクスポートを、YAML フロントマター付きのクリーンな Markdown に変換します。スレッドの分割、署名の除去、添付ファイルの抽出、カレンダーの解析に対応。1 ファイルでも 1 万ファイルでも同じように処理します。

✨ 機能

  • 忠実度の高い変換 — HTML のサニタイズ、Gmail/Outlook のスレッド分割、インライン画像の処理、カレンダーイベントの要約

  • CLI — ファイルまたはディレクトリを指定するだけで実行できます

  • ローカル Web UI — ドラッグ&ドロップインポート、ウォッチモード、変換グレードバッジ、処理履歴、ジョブごとの診断情報を備えたダークなコマンドセンター風インターフェース

  • Inbox/Cabinet ワークフロー — .eml ファイルを Inbox にドロップすると、dead-letter が Markdown バンドルを Cabinet に整理します

  • インストール検証 — dead-letter doctor が実行環境をチェックします

  • 変換レポート — ファイルごとの診断情報を含むオプトインの JSON レポート。添付ファイルの参照数/保持数を自動化や監査に利用できます

  • MCP サーバー — Claude Desktop、Claude Code、Codex、その他の MCP クライアントと連携できます

  • Claude プラグイン — Claude Code または Cowork に 1 コマンドでインストールでき、4 つのスラッシュコマンド (/dead-letter:convert, /dead-letter:summarize, /dead-letter:triage, /dead-letter:cabinet) を提供します

  • Python API — from dead_letter import convert ですぐに使えます

Related MCP server: DingusMail

🧠 LLM パイプライン向けに設計

生の .eml ファイルは、後段の LLM や検索パイプラインにとってノイズの多い入力です。MIME ヘッダー、マルチパート境界、重複した HTML/プレーンテキスト本文、エンコードされた添付ファイルがすべてテキスト経路に混ざり込みます。

dead-letter はこれらを YAML フロントマター付きの Markdown に正規化するため、MIME 解析や base64 の後処理をしなくても、メッセージ本文とメタデータをチャンキングやインデックス作成に利用できます。デフォルトの convert() と convert_dir() は、メッセージごとに 1 つの .md を書き出し、添付ファイル名をフロントマターに保持します。

ファイルシステム上の成果物も分離したい場合は、バンドルおよび Cabinet ワークフローが message.md と、保持されたデコード済みファイルを attachments/ の下に書き出します。Markdown はテキスト取り込みにそのまま使え、PDF、スプレッドシート、カレンダーファイル、その他の保持されたバイナリ添付ファイルは、既存の後段パーサー向けにきれいに分離されたままになります。

LLM との直接統合には、MCP サーバーを使うと、Claude Desktop、Claude Code、Codex、その他の MCP クライアントがシェルを介さずに dead-letter の変換ツールを呼び出せます。

📊 トークンコストのベンチマーク

dead-letter の価値は、あらゆる代替手段よりトークン数が少ないことではありません。トークンあたりの忠実度、つまりメールを完全に保つ最も安価な表現であることです。HTML スレッド、添付ファイル、ニュースレターからなる合成コーパスで測定しました(トークナイザー o200k_base、中央値)。

  • 生の .eml よりトークン数が約 88% 少ない — PDF 添付ファイル付きのメール 1 通は、生の状態で約 126k トークン、変換後は約 180 トークンです。

  • メールを完全に保つ唯一の表現 — スレッド構造、メッセージごとの送信者属性、リンク、添付ファイルのメタデータがすべて保持されます。単純なテキスト抽出がより安価なのは、まさにこれらを捨てるからです(添付ファイルの保持は 0/2 に対して dead-letter は 2/2)。

このベンチマークは、どこで劣るのかも正直に示しています。添付ファイル、リンク、スレッド構造を捨てても構わないのであれば、単純な抽出の方がトークン数は少なくなります。完全な方法、完全なテーブル(それらの行を含む)、トークナイザーの開示、ワンコマンドでの再現手順は benchmarks/ にあります。

📦 インストール

Apple silicon macOS で Homebrew を使う場合:

brew tap BigCactusLabs/tap
brew install dead-letter

Homebrew フォーミュラはコア CLI のみをインストールします: dead-letter convert と dead-letter doctor。オプションの Web UI や MCP サーバーの依存関係スタックは意図的にバンドルしていません。

pip を使う場合:

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 server

UI や MCP を分離してインストールするには pipx を使います:

pipx install 'dead-letter[ui]'    # installs dead-letter and dead-letter-ui
pipx install 'dead-letter[mcp]'   # installs dead-letter and dead-letter-mcp

ソースからインストールする場合:

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

🚀 クイックスタート

CLI — 単一ファイルを変換:

dead-letter convert message.eml

ディレクトリ全体を変換:

dead-letter convert inbox/ --output out/

出力と一緒に JSON 変換レポートを生成:

dead-letter convert inbox/ --output out/ --report

--output を指定すると、レポートはその出力ディレクトリに .dead-letter-report.json として書き込まれます。--output を指定しない場合、ファイル変換ではソースメッセージの隣に、ディレクトリ変換では入力ディレクトリのルートにレポートが書き込まれます。

実行環境をチェック:

dead-letter doctor

ディレクトリ変換は .eml ファイルを再帰的にスキャンし、拡張子を大文字小文字を区別せずに照合します。解決後のターゲットが指定された入力ツリーの外に出るシンボリックリンクのファイルはスキップし、同じメッセージファイルに解決されるツリー内のシンボリックリンクエイリアスは重複排除します。

Web UI — ローカルサーバーを起動:

dead-letter-ui --host 127.0.0.1 --port 8765

http://127.0.0.1:8765 を開きます。初回起動時にはセットアップ画面が表示され、デフォルトの Inbox フォルダと Cabinet フォルダが提案されます。設定するかスキップして変換を開始します。.eml ファイルはドラッグ&ドロップまたはファイルピッカーでインポートします。単一ファイルのインポートはファイルモード、複数ファイルのドロップはディレクトリモードのバッチジョブとして 1 つ作成されます。混在したドロップでは、非 .eml ファイルをスキップする前に確認を求められます。バックエンドは単一・バッチの両方のアップロードで、ファイルあたり 100 MB のインポート上限を適用します。

ソースチェックアウトから実行する場合は、uv run を前置します:

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 .md

オプション付き:

from dead_letter import convert, ConvertOptions

result = convert("message.eml", options=ConvertOptions(
    strip_signatures=True,
    strip_quoted_headers=True,
))

署名画像(ロゴ、ソーシャルアイコン)とトラッキングピクセルを除去:

result = convert("message.eml", options=ConvertOptions(
    strip_signature_images=True,
    strip_tracking_pixels=True,
))

有効にすると、これらのフィルターはレンダリングされた Markdown から一致する画像を削除し、除去されたインライン署名/トラッキングアセットをバンドル添付ファイルの出力から除外します。

バンドル変換(Markdown + 添付ファイル + ソースを 1 つのディレクトリに):

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" は元の .eml をその場に保持します。省略した場合、convert_to_bundle() はデフォルトで source_handling="move" となり、ソースメッセージをバンドル内に移動します。

保持された抽出添付ファイルのファイル名は、attachments/ の下に書き込まれる前に、安全なベース名に正規化されます。

品質診断には、保持対象となる添付ファイルがあるメッセージについて、参照/保持された添付ファイル数が含まれるため、破棄された成果物を機械的に検出できます。Quality Diagnostics を参照してください。

バッチ:

from dead_letter import convert_dir

for r in convert_dir("inbox/", output="out/"):
    print(f"{'✓' if r.success else '✗'} {r.source.name}")

🔌 MCP サーバー

dead-letter には MCP サーバーが同梱されており、LLM クライアントはシェルを介さずに .eml ファイルを直接変換できます。

インストールして起動:

pip install dead-letter[mcp]
dead-letter-mcp

ソースチェックアウトから:

uv run --extra mcp dead-letter-mcp

Claude Desktop — claude_desktop_config.json に追加:

{
  "mcpServers": {
    "dead-letter": {
      "command": "uv",
      "args": ["--directory", "/path/to/dead-letter", "run", "--extra", "mcp", "dead-letter-mcp"]
    }
  }
}

Claude Code または Cowork(推奨 — Claude プラグイン):

/plugin marketplace add BigCactusLabs/bigcactuslabs-plugins
/plugin install dead-letter

このプラグインは MCP サーバーをバンドルしており(uvx 経由、pip install は不要 — PATH に uv があればよい)、4 つのスラッシュコマンドを追加します: /dead-letter:convert、/dead-letter:summarize、/dead-letter:triage、/dead-letter:cabinet。プラグインを通じて処理されるメールコンテンツは命令ではなく信頼できないデータとして扱われるため、メッセージに埋め込まれたツール使用、資格情報、外部送信の要求には従いません。ソースは plugin/ にあります。

マーケットプレイスは、公開された各プラグインのタグとコミットを固定します。リリース自動化は、バンドルされた MCP サーバーの正確な PyPI バージョンが公開された後にのみそのポインタを更新するため、Claude Code と Cowork は同じ再現可能なリリースを解決します。

Claude Code(手動 MCP 追加 — 代替手段):

claude mcp add dead-letter -- uv run --extra mcp dead-letter-mcp

Codex:

codex mcp add dead-letter -- uv run --extra mcp dead-letter-mcp
codex mcp list

codex mcp add コマンドでローカルの dead-letter MCP サーバーを登録し、codex mcp list で利用可能かどうかを確認します。

ツール

ツール

必須引数

戻り値

convert_eml

eml_path

Markdown テキスト。output_path が指定された場合はファイルも書き出します。

convert_eml_to_bundle

eml_path, bundle_root

bundle_path、markdown_path、attachment_paths を含む JSON。コピーのみ: 元の .eml は移動も削除もされません。

convert_directory

directory, output_directory

JSON サマリー。1 回の呼び出しにつき最大 50 個の .eml ファイル。

get_diagnostics

eml_path

品質と構造の JSON。永続的なものは書き込みません。

4 つすべてが preset(default、clean、verbose、raw)とフラグごとの上書きを受け付けます。MCP 専用の制約とエラーテキストの表を含む完全な契約は、docs/reference/v4-runtime-contracts.md を参照してください。

🗂 プロジェクト構成

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

🧪 テスト

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     # frontend

CI は PR と main または feat/** ブランチへのプッシュで、同じコマンドを使って 4 つすべてを実行します。さらに npx --yes @anthropic-ai/claude-code@2.1.145 plugin validate plugin/ と node --check src/dead_letter/frontend/static/app.js も実行します。

📚 ドキュメント

🔧 お気に入りのツール

  • MarkEdit — Markdown 向け TextEdit、macOS ネイティブ、約 4 MB。dead-letter の出力を、まるで最初からそこにあるかのように開きます。

  • mo — ブラウザでファイルをライブリロード付きでレンダリングするローカル Markdown ビューア。Cabinet を指定すれば、変換済みメールをフィードのように読めます。

⚠️ 既知の制限

  • ローカルのみ — リモートサーバーなし、認証なし

  • インメモリのジョブレジストリ(再起動すると状態はリセットされます)

  • シングルユーザー、シングルマシン

ライセンス

PolyForm Noncommercial 1.0.0 — 個人、教育、非営利での使用は無料です。商用利用には Big Cactus Labs からの別途ライセンスが必要です。

Available Tools

5 tools
convert_directoryConvert email folderA

Batch convert all .eml files in a directory to Markdown.

Recursively finds .eml files (at most 50 per call; larger directories are rejected before any conversion). output_directory is required: Markdown is written there, mirroring subfolders, and source .eml files are left in place. Returns a JSON summary with total, successes, failures, output_paths, and errors. Each errors entry contains file and error_code (defaulting to conversion_error), never raw exception text.

Use convert_eml to retrieve individual converted file content.

NameRequiredDescriptionDefault
presetNodefault
dry_runNo
directoryYes
thread_modeNolatest
thread_orderNooldest-first
include_raw_htmlNo
output_directoryYes
strip_signaturesNo
strip_disclaimersNo
embed_inline_imagesNo
include_all_headersNo
no_calendar_summaryNo
strip_quoted_headersNo
strip_tracking_pixelsNo
strip_signature_imagesNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A3.9/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: the 50-file cap with pre-conversion rejection, that source .eml files are left in place, that subfolders are mirrored into output_directory, and that errors carry a file plus a sanitized error_code rather than raw exception text. That is exactly the operational context an agent needs before spending a large batch run.

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?

Front-loads the core action, then constraints, then output contract, then the sibling pointer — each sentence carries distinct information with no filler. Length is proportionate to a 15-parameter batch tool.

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 schema exists, and the description still usefully summarizes the JSON summary shape, so return values are covered. However, for a tool with 15 options and an enum-based preset system, the complete absence of guidance on how the conversion options interact (preset vs individual strip_* flags) leaves a real gap.

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 0% across 15 parameters, so the description carries the full burden — but it only explains output_directory (required, receives Markdown, mirrors subfolders). The 12 conversion flags and 3 enums (preset, thread_mode, thread_order) are entirely undocumented, so an agent cannot tell what 'clean' vs 'verbose' preset or 'structured' thread_mode does.

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?

Opens with a specific verb and resource: 'Batch convert all .eml files in a directory to Markdown,' which immediately separates it from the single-file convert_eml. It does not explicitly contrast with convert_mbox or convert_eml_to_bundle, so sibling differentiation is only partial.

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?

Gives concrete usage context (recursive .eml discovery, hard cap of 50 files with upfront rejection) and routes the agent to convert_eml for reading back individual converted content. It stops short of stating when-not to use it, e.g. for mbox archives, which is left to inference from sibling names.

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

convert_emlConvert emailA

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.

Attachments are listed in the front matter but not written to disk; use convert_eml_to_bundle to save the decoded attachment files.

thread_mode="structured" adds a section per earlier reply or forwarded message. Gmail HTML forwards and unquoted plain-text forwards are kept in latest mode too; a plain-text forward inside ">" quoting is not.

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.

NameRequiredDescriptionDefault
presetNodefault
eml_pathYes
output_pathNo
thread_modeNolatest
thread_orderNooldest-first
include_raw_htmlNo
strip_signaturesNo
strip_disclaimersNo
embed_inline_imagesNo
include_all_headersNo
no_calendar_summaryNo
strip_quoted_headersNo
strip_tracking_pixelsNo
strip_signature_imagesNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond the annotations: it discloses the file-writing side effect of output_path, that attachments are listed but not written, the per-preset stripping behavior, and that individual flags override presets. The described write behavior is consistent with readOnlyHint=false and destructiveHint=false.

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?

Front-loaded with the primary behavior, then organized into short paragraphs and a bulleted preset list that is genuinely useful given the empty schema descriptions. Slightly verbose on the forward-quoting edge case, but nothing extraneous.

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?

Covers the entity, the output shape, the disk side effect, and the preset/flag interaction, and an output schema exists so return-value detail is not owed. The remaining gap is semantics for the unlisted boolean flags in a 14-parameter tool.

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 0%, so the description carries the param burden and it partially does: all four preset values and both thread_mode values are given meaning, and output_path behavior is stated. However, thread_order, no_calendar_summary, embed_inline_images, and several strip_* flags are never characterized, leaving half the surface to title-only inference.

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?

States a specific verb and resource plus the exact output format ('Convert a .eml email file to Markdown with YAML front matter'). It also names the sibling convert_eml_to_bundle and the distinct condition that selects it, so an agent can separate the two without reading schemas.

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?

Explicitly routes attachment-saving use cases to convert_eml_to_bundle and explains the thread_mode and preset choices in operational terms. It does not address when convert_directory or convert_mbox should be preferred over this single-file tool, leaving the batch-vs-single decision implied.

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

convert_eml_to_bundleConvert email to bundleA

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.

NameRequiredDescriptionDefault
presetNodefault
eml_pathYes
bundle_rootYes
thread_modeNolatest
thread_orderNooldest-first
source_handlingNocopy
include_raw_htmlNo
strip_signaturesNo
strip_disclaimersNo
embed_inline_imagesNo
include_all_headersNo
no_calendar_summaryNo
strip_quoted_headersNo
strip_tracking_pixelsNo
strip_signature_imagesNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A3.6/5.0
Behavior4/5

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

Annotations declare destructiveHint=false and readOnlyHint=false, and the description usefully adds that the original .eml is copied and left untouched, plus that unsupported modes are rejected rather than silently downgraded. It lists the returned fields, giving concrete expectations. No contradiction with annotations; only missing detail is failure behavior (e.g., existing bundle directory).

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 output artifact is described first, then the source_handling caveat, then the return shape — a sensible front-loaded order. Paragraph breaks are used well. Minor redundancy: the return JSON is listed despite an output schema existing.

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?

An output schema exists, so the return-value recap is not strictly needed, and the description covers artifact creation and the source-preservation contract. But with 15 parameters at 0% schema coverage and no mention of the preset/thread options that materially change output, an agent cannot choose parameters confidently from this definition alone.

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 0% across 15 parameters, and the description documents only source_handling. The four enums (preset, thread_mode, thread_order, source_handling) and the ten boolean knobs (strip_signatures, embed_inline_images, include_all_headers, etc.) are left entirely to name inference, so the description does not compensate for the coverage gap.

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 gives a specific verb and resource — convert a .eml file into a self-contained bundle of markdown plus extracted attachments — and clarifies what the output artifact is. It does not explicitly distinguish itself from sibling convert_eml, so the caller must infer the difference from the 'bundle' vs. single-conversion wording.

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?

It clearly states the MCP-level constraint: source_handling only accepts 'copy' here, and 'move'/'delete' must go through the CLI or Python API. That is a genuine when/when-not statement. It stops short of routing between this tool and convert_eml/convert_directory/convert_mbox, which is the remaining gap.

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

convert_mboxConvert MBOX archiveA

Convert one flat .mbox archive (e.g. Gmail Takeout) to Markdown files.

Bounded: the archive must be at most 256 MiB, and conversion stops after 1000 messages (the response then has truncated=true; use the dead-letter CLI for larger archives). Compressed archives and Apple Mail .mbox directories are rejected. output_directory is required; one .md per message (or one bundle directory when bundles=true) is written there with a collision-safe JSON report. The source archive is never modified.

Returns a JSON summary (processed, converted, skipped, failed, truncated, report_path, and at most 20 failure entries), never message content. Cancellation is not supported; byte/message caps are not a time limit.

NameRequiredDescriptionDefault
pathYes
presetNodefault
bundlesNo
dry_runNo
thread_modeNolatest
thread_orderNooldest-first
include_raw_htmlNo
output_directoryYes
strip_signaturesNo
strip_disclaimersNo
embed_inline_imagesNo
include_all_headersNo
no_calendar_summaryNo
strip_quoted_headersNo
strip_tracking_pixelsNo
strip_signature_imagesNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior5/5

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

Adds substantial context beyond annotations: hard 256 MiB and 1000-message caps, the truncated=true signal, the requirement that output_directory be supplied, one .md per message vs bundle directories, a collision-safe report, and the guarantee that the source archive is never modified. It also clarifies negative behavior (no cancellation, caps are not a time limit), which is exactly the kind of trap an agent would otherwise fall into.

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?

Front-loads the core purpose in the first sentence, then groups constraints, output behavior, and return shape into short paragraphs. Dense but every sentence carries information an agent needs; no filler or restatement of the name.

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?

Limits, rejection cases, side effects, and return shape are all covered, and an output schema exists so return values need no further explanation. The gap is the undocumented parameter surface: an agent can call this correctly with defaults, but cannot make informed use of the 14 optional formatting toggles.

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 0% across 16 parameters, so the description carries the full burden, yet it only touches output_directory (already marked required in schema) and bundles=true. Fourteen parameters — preset, dry_run, thread_mode, thread_order, include_raw_html, embed_inline_images, no_calendar_summary, and all the strip_* toggles — get no explanation of what they change or when to set them.

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?

States a specific verb and resource ('Convert one flat .mbox archive ... to Markdown files') with its scope ('one flat', example Gmail Takeout). The distinction from siblings is implicit but strong: convert_directory handles directories, which the description explicitly rejects, and the format (.mbox vs .eml) separates it from convert_eml.

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?

Gives explicit when-not conditions (compressed archives and Apple Mail .mbox directories are rejected) and a named alternative for out-of-scope work ('use the dead-letter CLI for larger archives'). It does not explicitly route between convert_eml/convert_directory, but the input-format constraint makes the choice unambiguous.

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

get_diagnosticsGet conversion diagnosticsA
Read-only

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.

NameRequiredDescriptionDefault
presetNodefault
eml_pathYes
thread_modeNolatest
thread_orderNooldest-first
include_raw_htmlNo
strip_signaturesNo
strip_disclaimersNo
embed_inline_imagesNo
include_all_headersNo
no_calendar_summaryNo
strip_quoted_headersNo
strip_tracking_pixelsNo
strip_signature_imagesNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A3.6/5.0
Behavior4/5

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

Consistent with readOnlyHint=true and openWorldHint=false, and adds value beyond them by stating no permanent files are written and enumerating the return-state vocabulary (normal/degraded/review_recommended) plus conditional keys. Falls short of disclosing limits, cost, or when 'degraded' should trigger a re-run.

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?

Front-loaded with purpose, then usage, then return contract; no filler. The long enumeration of return keys is slightly heavy given an output schema exists, but it is organized and readable.

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?

Return values are well covered even though an output schema exists, and safety context is adequate. However, for a tool with 13 parameters and zero schema descriptions, omitting any parameter guidance leaves a genuine gap an agent would hit when deciding preset/thread/strip behavior.

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?

Thirteen parameters with 0% schema description coverage, and the description explains none of them — preset, thread_mode, thread_order, and the eight strip_*/include_* flags are opaque. The only indirect hint is stripped_images appearing conditionally when images were removed, which barely signals the stripping flags.

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?

States a specific verb and resource ('Inspect email quality and structure') and differentiates from the convert_* siblings by noting it does not write permanent files. A clear read-only inspection tool, though it doesn't name the siblings explicitly.

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?

Gives two concrete situations: assess conversion quality before committing, or troubleshoot problematic .eml files. This implicitly routes agents away from convert_eml when they only want inspection, but no alternative is named outright.

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.

  1. 2 tool updatesv0.4.5
    • Changedconvert_directory4 fields changed
      • removedInput schema / properties / output_directory / anyOf
        Removed value: -[
        -  {
        -    "type": "string"
        -  },
        -  {
        -    "type": "null"
        -  }
        -]
      • removedInput schema / properties / output_directory / default
        Removed value: -null
      • addedInput schema / properties / output_directory / type
        Added value: +"string"
      • changedInput schema / required
        Previous value: -[
        -  "directory"
        -]New value: +[
        +  "directory",
        +  "output_directory"
        +]
    • Addedconvert_mbox
  2. 4 tool updatesv0.2.4
    • First observedconvert_directory
    • First observedconvert_eml
    • First observedconvert_eml_to_bundle
    • First observedget_diagnostics

TDQS

A4/5.0

Scored across 5 tools

Disambiguation4/5

convert_eml and convert_eml_to_bundle are closely related but differentiated by bundle/attachment output, while convert_directory and convert_mbox are separated by input type. get_diagnostics is clearly distinct, leaving only minor ambiguity between single-file conversion variants.

Naming Consistency5/5

All tool names use consistent snake_case and are verb-led, with the convert_* family forming a predictable group and get_diagnostics fitting naturally as a read-only operation. The extra preposition in convert_eml_to_bundle is still clear and consistent.

Tool Count5/5

Five tools form a well-scoped surface for email conversion: single-file, bundle, directory batch, mbox batch, and diagnostics. Each tool has a clear reason to exist without obvious bloat.

Completeness4/5

The surface covers the core conversion workflows, including attachments via bundles and quality inspection via diagnostics. Minor gaps remain around reading back already-converted outputs, and some archive modes are deliberately rejected and deferred to the CLI/API.

Maintenance

ActivityActive
ResponsivenessWithin a week

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