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Disco

表形式データから、統計的に検証された新しいパターンを発見する — 人間やエージェントが見逃してしまう特徴量の相互作用、サブグループの影響、条件付き関係を明らかにします。

PyPI License: MIT

Leap Laboratories によって作成されました。


実際に何ができるのか

ほとんどのデータ分析は「問い」から始まりますが、Discoは「データ」から始まります。

バイアスや前提条件なしに、ターゲット列を大きく変化させる特徴量条件の組み合わせを見つけ出します。例えば、「HDLが低く、かつCRPが高い45〜65歳の患者は、再入院率が3倍になる」といったパターンを、事前に仮説を立てることなく発見します。

各パターンは以下の通りです:

  • ホールドアウトセットで検証済み — 一般化の可能性を高めます

  • FDR補正済み — 多重比較を調整したp値が含まれます

  • 学術文献と照合済み — 発見した内容を理解し、それが新しいものかどうかを特定するのに役立ちます

出力は、条件、効果量、p値、引用文献、および発見されたすべてのパターンの新規性分類で構成されています。

次のような場合に使用してください: 「Xに関して最も重要な変数は何か」「見逃しているパターンはないか」「このデータで何から始めればよいかわからない」「AとBがCにどのように影響するかを理解したい」

次のような用途には向きません: 要約統計量、可視化、フィルタリング、SQLクエリ(これらにはpandasを使用してください)


Related MCP server: Discovery Engine MCP Server

クイックスタート

pip install discovery-engine-api

APIキーを取得する:

# Step 1: request verification code (no password, no card)
curl -X POST https://disco.leap-labs.com/api/signup \
  -H "Content-Type: application/json" \
  -d '{"email": "you@example.com"}'

# Step 2: submit code from email → get key
curl -X POST https://disco.leap-labs.com/api/signup/verify \
  -H "Content-Type: application/json" \
  -d '{"email": "you@example.com", "code": "123456"}'
# → {"key": "disco_...", "credits": 10, "tier": "free_tier"}

または、disco.leap-labs.com/developers でキーを作成してください。

最初の分析を実行する:

from discovery import Engine

engine = Engine(api_key="disco_...")
result = await engine.discover(
    file="data.csv",
    target_column="outcome",
)

for pattern in result.patterns:
    if pattern.p_value < 0.05 and pattern.novelty_type == "novel":
        print(f"{pattern.description} (p={pattern.p_value:.4f})")

print(f"Explore: {result.report_url}")

実行には数分かかります。discover() は自動的にポーリングを行い、進捗状況(キューの位置、推定待ち時間、現在のパイプラインステップ、ETA)をログに記録します。バックグラウンド実行については、非同期実行を参照してください。

→ Python SDKリファレンス(完全版) · サンプルノートブック


得られる結果

result.patterns 内の各 Pattern は以下のようになります(作物収量データセットからの実際の出力):

Pattern(
    description="When humidity is between 72–89% AND wind speed is below 12 km/h, "
                "crop yield increases by 34% above the dataset average",
    conditions=[
        {"type": "continuous", "feature": "humidity_pct",
         "min_value": 72.0, "max_value": 89.0},
        {"type": "continuous", "feature": "wind_speed_kmh",
         "min_value": 0.0, "max_value": 12.0},
    ],
    p_value=0.003,              # FDR-corrected
    novelty_type="novel",
    novelty_explanation="Published studies examine humidity and wind speed as independent "
                        "predictors, but this interaction effect — where low wind amplifies "
                        "the benefit of high humidity within a specific range — has not been "
                        "reported in the literature.",
    citations=[
        {"title": "Effects of relative humidity on cereal crop productivity",
         "authors": ["Zhang, L.", "Wang, H."], "year": "2021",
         "journal": "Journal of Agricultural Science"},
    ],
    target_change_direction="max",
    abs_target_change=0.34,     # 34% increase
    support_count=847,          # rows matching this pattern
    support_percentage=16.9,
)

注目すべき重要な点:

  • パターンは条件の組み合わせ — 単に「湿度が高いほど良い」ではなく、湿度と風速の組み合わせ

  • 具体的なしきい値 — 曖昧な相関ではなく、72〜89%といった具体的な数値

  • 新規性 vs 確認的 — すべてのパターンが分類されます。確認的なものは既知の科学を検証し、新規なものはまさにあなたが求めていたものです

  • 引用文献 — 何が既知であるかを示し、何が真に新しい発見であるかを確認できます

  • report_url — すべてのパターンが可視化されたインタラクティブなWebレポートへのリンク

result.summary は、LLMが生成した物語形式の概要を提供します:

result.summary.overview
# "Disco identified 14 statistically significant patterns. 5 are novel.
#  The strongest driver is a previously unreported interaction between humidity
#  and wind speed at specific thresholds."

result.summary.key_insights
# ["Humidity × low wind speed at 72–89% humidity produces a 34% yield increase — novel.",
#  "Soil nitrogen above 45 mg/kg shows diminishing returns when phosphorus is below 12 mg/kg.",
#  ...]

仕組み

Discoはデータに対するプロンプトエンジニアリングではなく、パイプラインです。以下の手順を実行します:

  1. データのサブセットで機械学習モデルをトレーニングする

  2. 解釈可能性技術を使用して学習済みパターンを抽出する

  3. FDR補正(ベンジャミーニ・ホフバーグ法)を用いて、ホールドアウトデータですべてのパターンを検証する

  4. セマンティック検索を通じて、生存したパターンを学術文献と照合する

これは、pandasコードを書いたり、LLMにCSVを見せたりするだけでは再現できません。仮説主導型の分析では見逃されてしまう構造を、仮説から始めないことで発見します。


データの準備

実行前に、無意味な結果を生む可能性のある列を除外してください。Discoは統計的に実在するパターンを見つけますが、入力データにターゲットと定義上関連する列が含まれていると、パターンはトートロジー(同語反復)になります。

除外すべきもの:

  1. 識別子 — 行ID、UUID、患者ID、サンプルコード

  2. データリーク — 名前が変更または再フォーマットされたターゲット(例:ターゲットが diagnosis_code の場合の diagnosis_text)

  3. トートロジー列 — ターゲットと同じ概念の代替エンコーディング。ターゲットが serious ならば、serious_outcome、not_serious、death はすべて同じ分類の一部です。ターゲットが profit ならば、revenue と cost を合わせるとターゲットになります。ターゲットが調査インデックスの場合、サブ項目はトートロジーです。

例を含む完全なガイダンス:SKILL.md


パラメータ

await engine.discover(
    file="data.csv",           # path, Path, or pd.DataFrame
    target_column="outcome",   # column to predict/explain
    analysis_depth=2,          # 2=default, higher=deeper analysis, lower = faster and cheaper
    visibility="public",       # "public" (always free, data and report is published) or "private" (costs credits)
    column_descriptions={      # improves pattern explanations and literature context
        "bmi": "Body mass index",
        "hdl": "HDL cholesterol in mg/dL",
    },
    excluded_columns=["id", "timestamp"],  # see "Preparing your data" above
    use_llms=False,                        # Defaults to False. If True, runs are slower and more expensive, but you get smarter pre-processing, summary page, literature context and novelty assessment. Public runs always use LLMs.
    title="My dataset",
    description="...", # improves pattern explanations and literature context
)

パブリック実行は無料ですが、結果は公開されます。プライベートデータの場合は visibility="private" を設定してください(クレジットを消費します)。


非同期実行

実行には数分かかります。エージェントワークフローや、並行して他の作業を行うスクリプトの場合:

# Submit without waiting
run = await engine.run_async(file="data.csv", target_column="outcome", wait=False)
print(f"Submitted {run.run_id}, continuing...")

# ... do other things ...

result = await engine.wait_for_completion(run.run_id, timeout=1800)

同期スクリプトやJupyterノートブックの場合:

result = engine.run(file="data.csv", target_column="outcome", wait=True)
# or: pip install discovery-engine-api[jupyter] for notebook compatibility

MCPサーバー

DiscoはMCPサーバーとして利用可能です。ローカルインストールは不要です。

{
  "mcpServers": {
    "discovery-engine": {
      "url": "https://disco.leap-labs.com/mcp",
      "env": { "DISCOVERY_API_KEY": "disco_..." }
    }
  }
}

ツール:discovery_list_plans, discovery_estimate, discovery_upload, discovery_analyze, discovery_status, discovery_get_results, discovery_account, discovery_signup, discovery_signup_verify, discovery_login, discovery_login_verify, discovery_add_payment_method, discovery_subscribe, discovery_purchase_credits

→ エージェントスキルファイル(完全版)


料金

コスト

パブリック実行

無料 — 結果とデータは公開されます

プライベート実行

ファイルサイズと構成によりクレジットが異なります — engine.estimate() を使用してください

無料ティア

月10クレジット、カード不要

リサーチャー

月額$49 — 50クレジット

チーム

月額$199 — 200クレジット

クレジット

1クレジットあたり$0.10

実行前に見積もりを行う:

estimate = await engine.estimate(file_size_mb=10.5, num_columns=25, analysis_depth=2, visibility="private")
# estimate["cost"]["credits"] → 55
# estimate["account"]["sufficient"] → True/False

アカウント管理は完全にプログラム可能です。SDKまたはREST APIを使用して、支払い方法の追加、プランの購読、クレジットの購入が可能です。Python SDKリファレンス または SKILL.md を参照してください。


期待されるデータ形式

Discoは フラットなテーブル を想定しています(列は特徴量、行はサンプル)。

| patient_id | age | bmi  | smoker | outcome |
|------------|-----|------|--------|---------|
| 001        | 52  | 28.3 | yes    | 1       |
| 002        | 34  | 22.1 | no     | 0       |
| ...        | ... | ...  | ...    | ...     |
  • 1観測につき1行 — 患者、サンプル、トランザクション、測定値など

  • 1特徴量につき1列 — 数値、カテゴリ、日時、フリーテキストのいずれも可

  • 1ターゲット列 — 理解したい結果。少なくとも2つの異なる値が必要です

  • 欠損値はOK — Discoが自動的に処理します。事前に行を削除したり補完したりしないでください

  • ピボット不要 — データがすでにフラットなテーブル形式であれば、そのまま使用できます

サポートされている形式: CSV、TSV、Excel (.xlsx)、JSON、Parquet、ARFF、Feather。最大5GB。

サポートされていないもの: 画像、生のテキストドキュメント、ネストされた/階層的なJSON、マルチシートのExcel(最初のシートを使用するか、CSVにエクスポートしてください)


他のツールとの比較

目的

ツール

要約統計量、データ品質

ydata-profiling, sweetviz

予測モデル

AutoML (auto-sklearn, TPOT, H2O)

クイックな相関分析

pandas, seaborn

データに関する特定の質問への回答

ChatGPT, Claude

何を探すべきかわからないものを発見する

Disco

DiscoはEDAやAutoMLの代替ではありません。それらのツールが見逃してしまうパターンを発見します。既知の正解パターンを持つデータセットで 18種類のデータ分析ツールをテスト しました。ほとんどのツールは誤った結果を自信満々に報告しましたが、すべてのパターンを発見できたのはDiscoだけでした。


リンク


Available Tools

14 tools
discovery_accountA
Read-only
Inspect

Check your Disco account status.

Returns current plan, available credits (subscription + purchased), and
payment method status. Use this to verify you have sufficient credits
before running a private analysis.

Args:
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: api_key. Returns: result

discovery_add_payment_methodA
Idempotent
Inspect

Attach a Stripe payment method to your Disco account.

The payment method must be tokenized via Stripe's API first — card details
never touch Disco's servers. Required before purchasing credits
or subscribing to a paid plan.

To tokenize a card, call Stripe's API directly:
POST https://api.stripe.com/v1/payment_methods
with the stripe_publishable_key from your account info.

Args:
    payment_method_id: Stripe payment method ID (pm_...) from Stripe's API.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: payment_method_id (required), api_key. Returns: result

discovery_analyzeA
Destructive
Inspect

Run Disco on tabular data to find novel, statistically validated patterns.

This is NOT another data analyst — it's a discovery pipeline that systematically
searches for feature interactions, subgroup effects, and conditional relationships
nobody thought to look for, then validates each on hold-out data with FDR-corrected
p-values and checks novelty against academic literature.

This is a long-running operation. Returns a run_id immediately.
Use discovery_status to poll and discovery_get_results to fetch completed results.

Use this when you need to go beyond answering questions about data and start
finding things nobody thought to ask. Do NOT use this for summary statistics,
visualization, or SQL queries.

Public runs are free but results are published. Private runs cost credits.
Call discovery_estimate first to check cost. Private report URLs require
sign-in — tell the user to sign in at the dashboard with the same email
address used to create the account (email code, no password needed).

Call discovery_upload first to upload your file, then pass the returned file_ref here.

Args:
    target_column: The column to analyze — what drives it, beyond what's obvious.
    file_ref: The file reference returned by discovery_upload.
    analysis_depth: Search depth (1=fast, higher=deeper). Default 1.
    visibility: "public" (free) or "private" (costs credits). Default "public".
    title: Optional title for the analysis.
    description: Optional description of the dataset.
    excluded_columns: Optional JSON array of column names to exclude from analysis.
    column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}).
    author: Optional author name for the report.
    source_url: Optional source URL for the dataset.
    use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: target_column (required), file_ref, analysis_depth, visibility, +8 more. Returns: result

discovery_estimateA
Read-only
Inspect

Estimate cost, time, and credit requirements before running an analysis.

Returns credit cost, estimated duration in seconds, whether you have
sufficient credits, and whether a free public alternative exists. Always call
this before discovery_analyze for private runs.

Args:
    file_size_mb: Size of the dataset in megabytes.
    num_columns: Number of columns in the dataset.
    num_rows: Number of rows (optional, improves time estimate).
    analysis_depth: Search depth (1=fast, higher=deeper). Default 1.
    visibility: "public" (free, results published) or "private" (costs credits).
    use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: file_size_mb (required), num_columns (required), num_rows, analysis_depth, +3 more. Returns: result

discovery_get_resultsA
Read-only
Inspect

Fetch the full results of a completed Disco run.

Returns discovered patterns (with conditions, p-values, novelty scores,
citations), feature importance scores, a summary with key insights, column
statistics, and suggestions for what to explore next.

The response includes a `dashboard_urls` object with direct links to each
page of the interactive report — use these to direct the user to the most
relevant view:
- **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings
- **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualisations
- **features**: Feature importances, feature statistics and distribution plots, and correlation matrix
- **territory**: Interactive 3D map showing how patterns select different regions of the data

Only call this after discovery_status returns "completed".

Args:
    run_id: The run ID returned by discovery_analyze.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: run_id (required), api_key. Returns: result

discovery_list_plansA
Read-only
Inspect

List available Disco plans with pricing.

No authentication required. Returns all available subscription tiers with credit allowances and pricing. Use this to help users choose a plan.

Inputs: no named parameters. Returns: result

discovery_loginA
Idempotent
Inspect

Get a new API key for an existing Disco account.

Sends a 6-digit verification code to the email address. Call
discovery_login_verify with the code to receive a new API key.
Use this when you need an API key for an account that already exists
(e.g. the key was lost or this is a new agent session).

Returns 404 if no account exists with this email — use discovery_signup instead.

Args:
    email: Email address of the existing account.

Inputs: email (required). Returns: result

discovery_login_verifyA
Idempotent
Inspect

Complete login and receive a new API key.

Call this after discovery_login returns {"status": "verification_required"}.
The user receives a 6-digit code by email — pass it here along with the
same email address. Returns a new API key on success.

Args:
    email: Email address used in the discovery_login call.
    code: 6-digit verification code from the email.

Inputs: email (required), code (required). Returns: result

discovery_purchase_creditsA
Destructive
Inspect

Purchase Disco credit packs using a stored payment method.

Credits cost $0.10 each, sold in packs of 100 ($10/pack). Credits are used
for private analyses (public analyses are free). Requires a payment method
on file — use discovery_add_payment_method first.

Args:
    packs: Number of 100-credit packs to purchase. Default 1.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: packs, api_key. Returns: result

discovery_signupA
Idempotent
Inspect

Create a Disco account and get an API key.

Provide an email address to start the signup flow. If email verification
is required, returns {"status": "verification_required"} — the user will
receive a 6-digit code by email, then call discovery_signup_verify to
complete signup and receive the API key. The free tier (10 credits/month,
unlimited public runs) is active immediately. No authentication required.

Returns 409 if the email is already registered.

Args:
    email: Email address for the new account.
    name: Display name (optional — defaults to email local part).

Inputs: email (required), name. Returns: result

discovery_signup_verifyA
Idempotent
Inspect

Complete Disco signup using an email verification code.

Call this after discovery_signup returns {"status": "verification_required"}.
The user receives a 6-digit code by email — pass it here along with the
same email address used in discovery_signup. Returns an API key on success.

Args:
    email: Email address used in the discovery_signup call.
    code: 6-digit verification code from the email.

Inputs: email (required), code (required). Returns: result

discovery_statusA
Read-only
Inspect

Check the status of a Disco run.

Returns current status and progress details:
- status: "pending" | "processing" | "completed" | "failed"
- job_status: underlying job queue status
- queue_position: position in queue when pending (1 = next up)
- current_step: active pipeline step (preprocessing, training, interpreting, reporting)
- estimated_seconds: estimated total processing time in seconds
- estimated_wait_seconds: estimated queue wait time in seconds (pending only)

Poll this after calling discovery_analyze — runs typically take 3–15 minutes.
Use discovery_get_results to fetch full results once status is "completed".

Args:
    run_id: The run ID returned by discovery_analyze.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: run_id (required), api_key. Returns: result

discovery_subscribeA
DestructiveIdempotent
Inspect

Subscribe to or change your Disco plan.

Available plans:
- "free_tier": Explorer — free, 10 credits/month
- "tier_1": Researcher — $49/month, 50 credits/month
- "tier_2": Team — $199/month, 200 credits/month

Paid plans require a payment method on file. Credits roll over on paid plans.

Args:
    plan: Plan tier ID ("free_tier", "tier_1", or "tier_2").
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: plan (required), api_key. Returns: result

discovery_uploadAInspect

Upload a dataset file and return a file reference for use with discovery_analyze.

Call this before discovery_analyze. Pass the returned result directly to
discovery_analyze as the file_ref argument.

Provide exactly one of: file_url, file_path, or file_content.

Args:
    file_url: A publicly accessible http/https URL. The server downloads it directly.
              Best option for remote datasets.
    file_path: Absolute path to a local file. Only works when running the MCP server
               locally (not the hosted version). Streams the file directly — no size limit.
    file_content: File contents, base64-encoded. For small files when a URL or path
                  isn't available. Limited by the model's context window.
    file_name: Filename with extension (e.g. "data.csv"), for format detection.
               Only used with file_content. Default: "data.csv".
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Inputs: file_content, file_name, file_path, file_url, +1 more. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.7/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap. Account management (discovery_account, discovery_add_payment_method), authentication (discovery_login, discovery_login_verify, discovery_signup, discovery_signup_verify), analysis workflow (discovery_upload, discovery_estimate, discovery_analyze, discovery_status, discovery_get_results), and billing (discovery_list_plans, discovery_purchase_credits, discovery_subscribe) are all cleanly separated. An agent can easily distinguish between tools like discovery_analyze (run analysis) and discovery_estimate (check cost/time) despite both relating to analysis preparation.

    Naming Consistency5/5

    All 14 tools follow a perfect 'discovery_verb_noun' pattern with consistent snake_case throughout. The naming convention is highly predictable: discovery_account, discovery_analyze, discovery_estimate, discovery_get_results, discovery_list_plans, etc. This consistency makes the tool set immediately understandable and navigable.

    Tool Count5/5

    14 tools is ideal for this server's comprehensive scope covering authentication, data upload, analysis execution, results retrieval, and billing management. Each tool serves a specific, necessary function in the end-to-end workflow. The count is neither too thin (missing critical operations) nor bloated (no redundant tools), perfectly matching the domain of a sophisticated data analysis platform.

    Completeness5/5

    The tool surface provides complete coverage for the Disco platform's domain. It includes full authentication flow (signup/login with verification), account management, billing operations (plans, payments, credits), data upload, analysis estimation, execution, status monitoring, and results retrieval. There are no dead ends or gaps—every logical step in the workflow has a corresponding tool, creating a coherent end-to-end experience.

    Maintenance

    ActivitySlowing
    ResponsivenessLimited data

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