Disco
Disco
Finden Sie neuartige, statistisch validierte Muster in tabellarischen Daten — Merkmalsinteraktionen, Untergruppeneffekte und bedingte Beziehungen, die Menschen und Agenten übersehen.
Erstellt von Leap Laboratories.
Was es tatsächlich tut
Die meisten Datenanalysen beginnen mit einer Frage. Disco beginnt mit den Daten.
Ohne Vorurteile oder Annahmen findet es Kombinationen von Merkmalsbedingungen, die Ihre Zielspalte signifikant verschieben — Dinge wie „Patienten im Alter von 45–65 Jahren mit niedrigem HDL und hohem CRP haben eine 3× höhere Wiederaufnahmerate“ — ohne dass Sie diese Interaktion vorher hypothetisieren müssen.
Jedes Muster ist:
Auf einem Hold-out-Set validiert — erhöht die Chance auf Generalisierung
FDR-korrigiert — p-Werte enthalten, angepasst für multiples Testen
Gegen die akademische Literatur geprüft — um Ihnen zu helfen, zu verstehen, was Sie gefunden haben, und festzustellen, ob es neuartig ist.
Die Ausgabe ist strukturiert: Bedingungen, Effektstärken, p-Werte, Zitate und eine Neuheitsklassifizierung für jedes gefundene Muster.
Verwenden Sie es, wenn: „Welche Variablen sind in Bezug auf X am wichtigsten?“, „Gibt es Muster, die wir übersehen?“, „Ich weiß nicht, wo ich mit diesen Daten anfangen soll“, „Ich muss verstehen, wie A und B C beeinflussen“.
Nicht für: zusammenfassende Statistiken, Visualisierung, Filterung, SQL-Abfragen — verwenden Sie dafür pandas
Related MCP server: Discovery Engine MCP Server
Schnellstart
pip install discovery-engine-apiHolen Sie sich einen API-Schlüssel:
# 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"}Oder erstellen Sie einen Schlüssel unter disco.leap-labs.com/developers.
Führen Sie Ihre erste Analyse aus:
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}")Die Ausführung dauert einige Minuten. discover() fragt automatisch ab und protokolliert den Fortschritt — Warteschlangenposition, geschätzte Wartezeit, aktueller Pipeline-Schritt und ETA. Für Hintergrundausführungen siehe Asynchron ausführen.
→ Vollständige Python SDK-Referenz · Beispiel-Notebook
Was Sie zurückerhalten
Jedes Pattern in result.patterns sieht so aus (echte Ausgabe aus einem Datensatz zu Ernteerträgen):
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,
)Wichtige Punkte, die zu beachten sind:
Muster sind Kombinationen von Bedingungen — Luftfeuchtigkeit UND Windgeschwindigkeit zusammen, nicht nur „mehr Luftfeuchtigkeit ist besser“
Spezifische Schwellenwerte — 72–89 %, keine vage Korrelation
Neuartig vs. bestätigend — jedes Muster wird klassifiziert; bestätigende validieren bekannte Wissenschaft, neuartige sind das, wonach Sie gesucht haben
Zitate — zeigen, was bekannt IST, damit Sie sehen können, was wirklich neu ist
report_urlverlinkt auf einen interaktiven Webbericht, in dem alle Muster visualisiert sind
Das result.summary liefert einen LLM-generierten narrativen Überblick:
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.",
# ...]Wie es funktioniert
Disco ist eine Pipeline, kein Prompt-Engineering über Daten. Es:
Trainiert Machine-Learning-Modelle auf einer Teilmenge Ihrer Daten
Verwendet Interpretierbarkeitstechniken, um gelernte Muster zu extrahieren
Validiert jedes Muster auf den zurückgehaltenen Daten mit FDR-Korrektur (Benjamini-Hochberg)
Überprüft überlebende Muster anhand der akademischen Literatur mittels semantischer Suche
Sie können dies nicht durch das Schreiben von pandas-Code oder die Aufforderung an ein LLM, eine CSV-Datei zu betrachten, replizieren. Es findet Strukturen, die hypothesengetriebene Analysen übersehen, weil es nicht mit Hypothesen beginnt.
Vorbereitung Ihrer Daten
Schließen Sie vor der Ausführung Spalten aus, die bedeutungslose Ergebnisse liefern würden. Disco findet statistisch reale Muster — aber wenn die Eingabe Spalten enthält, die definitionsgemäß mit dem Ziel zusammenhängen, sind die Muster tautologisch.
Ausschließen:
Identifikatoren — Zeilen-IDs, UUIDs, Patienten-IDs, Probencodes
Datenlecks — das Ziel umbenannt oder neu formatiert (z. B.
diagnosis_text, wenn das Zieldiagnosis_codeist)Tautologische Spalten — alternative Kodierungen desselben Konstrukts wie das Ziel. Wenn das Ziel
seriousist, dann sindserious_outcome,not_serious,deathalle Teil derselben Klassifizierung. Wenn das Zielprofitist, dann setzen sichrevenueundcostzusammen daraus zusammen. Wenn das Ziel ein Umfrageindex ist, sind die Unterpunkte tautologisch.
Vollständige Anleitung mit Beispielen: SKILL.md
Parameter
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
)Öffentliche Ausführungen sind kostenlos, aber die Ergebnisse werden veröffentlicht. Setzen Sie
visibility="private"für private Daten — dies kostet Credits.
Asynchron ausführen
Die Ausführung dauert einige Minuten. Für Agenten-Workflows oder Skripte, die andere Arbeiten parallel ausführen:
# 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)Für synchrone Skripte und Jupyter-Notebooks:
result = engine.run(file="data.csv", target_column="outcome", wait=True)
# or: pip install discovery-engine-api[jupyter] for notebook compatibilityMCP-Server
Disco ist als MCP-Server verfügbar — keine lokale Installation erforderlich.
{
"mcpServers": {
"discovery-engine": {
"url": "https://disco.leap-labs.com/mcp",
"env": { "DISCOVERY_API_KEY": "disco_..." }
}
}
}Tools: 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.
→ Vollständige Agent-Skill-Datei
Preisgestaltung
Kosten | |
Öffentliche Ausführungen | Kostenlos — Ergebnisse und Daten werden veröffentlicht |
Private Ausführungen | Credits variieren je nach Dateigröße und Konfiguration — verwenden Sie |
Kostenloser Tarif | 10 Credits/Monat, keine Karte erforderlich |
Forscher | $49/Monat — 50 Credits |
Team | $199/Monat — 200 Credits |
Credits | $0,10 pro Credit |
Schätzung vor der Ausführung:
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/FalseDie Kontoverwaltung erfolgt vollständig programmatisch — fügen Sie Zahlungsmethoden hinzu, abonnieren Sie Pläne und kaufen Sie Credits über das SDK oder die REST-API. Siehe Python SDK-Referenz oder SKILL.md.
Erwartetes Datenformat
Disco erwartet eine flache Tabelle — Spalten für Merkmale, Zeilen für Stichproben.
| patient_id | age | bmi | smoker | outcome |
|------------|-----|------|--------|---------|
| 001 | 52 | 28.3 | yes | 1 |
| 002 | 34 | 22.1 | no | 0 |
| ... | ... | ... | ... | ... |Eine Zeile pro Beobachtung — ein Patient, eine Probe, eine Transaktion, eine Messung usw.
Eine Spalte pro Merkmal — numerisch, kategorisch, Datum/Uhrzeit oder Freitext sind alle in Ordnung
Eine Zielspalte — das Ergebnis, das Sie verstehen möchten. Muss mindestens 2 verschiedene Werte haben.
Fehlende Werte sind OK — Disco verarbeitet sie automatisch. Löschen Sie keine Zeilen und imputieren Sie nicht im Voraus.
Kein Pivoting erforderlich — wenn Ihre Daten bereits in einer flachen Tabelle vorliegen, sind sie bereit.
Unterstützte Formate: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max. 5 GB.
Nicht unterstützt: Bilder, reine Textdokumente, verschachteltes/hierarchisches JSON, Excel mit mehreren Tabellenblättern (verwenden Sie das erste Blatt oder exportieren Sie als CSV)
Vergleich mit anderen Tools
Ziel | Tool |
Zusammenfassende Statistiken, Datenqualität | ydata-profiling, sweetviz |
Prädiktives Modell | AutoML (auto-sklearn, TPOT, H2O) |
Schnelle Korrelationen | pandas, seaborn |
Beantwortung einer spezifischen Frage zu Daten | ChatGPT, Claude |
Finden, wonach Sie nicht zu suchen wissen | Disco |
Disco ist kein Ersatz für EDA oder AutoML — es findet die Muster, die diese Tools übersehen. Wir haben 18 Datenanalysetools getestet an einem Datensatz mit bekannten Ground-Truth-Mustern. Die meisten meldeten fälschlicherweise Ergebnisse. Disco war das einzige, das jedes Muster fand.
Links
Available Tools
14 toolsdiscovery_accountARead-onlyInspect
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_methodAIdempotentInspect
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_analyzeADestructiveInspect
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_estimateARead-onlyInspect
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_resultsARead-onlyInspect
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_plansARead-onlyInspect
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_loginAIdempotentInspect
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_verifyAIdempotentInspect
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_creditsADestructiveInspect
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_signupAIdempotentInspect
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_verifyAIdempotentInspect
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_statusARead-onlyInspect
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_subscribeADestructiveIdempotentInspect
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
Scored across 14 tools
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.
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.
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.
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.
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