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Disco

Encuentra patrones novedosos y validados estadísticamente en datos tabulares: interacciones de características, efectos de subgrupos y relaciones condicionales que los humanos y los agentes pasan por alto.

PyPI License: MIT

Creado por Leap Laboratories.


Qué hace realmente

La mayoría de los análisis de datos comienzan con una pregunta. Disco comienza con los datos.

Sin sesgos ni suposiciones, encuentra combinaciones de condiciones de características que alteran significativamente tu columna objetivo (cosas como "los pacientes de 45 a 65 años con HDL bajo y CRP alto tienen 3 veces la tasa de reingreso") sin que necesites formular la hipótesis de esa interacción primero.

Cada patrón está:

  • Validado en un conjunto de prueba (hold-out): aumenta la probabilidad de generalización.

  • Corregido por FDR: incluye valores p, ajustados para pruebas múltiples.

  • Contrastado con la literatura académica: para ayudarte a entender lo que has encontrado e identificar si es novedoso.

El resultado está estructurado: condiciones, tamaños del efecto, valores p, citas y una clasificación de novedad para cada patrón encontrado.

Úsalo cuando: "qué variables son más importantes con respecto a X", "¿hay patrones que nos estamos perdiendo?", "no sé por dónde empezar con estos datos", "necesito entender cómo A y B afectan a C".

No para: estadísticas resumidas, visualización, filtrado, consultas SQL; usa pandas para eso.


Related MCP server: Discovery Engine MCP Server

Inicio rápido

pip install discovery-engine-api

Obtén una clave API:

# 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"}

O crea una clave en disco.leap-labs.com/developers.

Ejecuta tu primer análisis:

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}")

Las ejecuciones tardan unos minutos. discover() realiza sondeos automáticamente y registra el progreso: posición en la cola, espera estimada, paso actual del pipeline y tiempo estimado de llegada (ETA). Para ejecuciones en segundo plano, consulta Ejecución asíncrona.

→ Referencia completa del SDK de Python · Cuaderno de ejemplo


Qué obtienes a cambio

Cada Pattern en result.patterns se ve así (resultado real de un conjunto de datos de rendimiento de cultivos):

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,
)

Aspectos clave a tener en cuenta:

  • Los patrones son combinaciones de condiciones: humedad Y velocidad del viento juntas, no solo "más humedad es mejor".

  • Umbrales específicos: 72–89%, no una correlación vaga.

  • Novedoso vs confirmatorio: cada patrón está clasificado; los confirmatorios validan la ciencia conocida, los novedosos son lo que buscabas.

  • Citas: muestra lo que YA se sabe, para que puedas ver qué es genuinamente nuevo.

  • report_url enlaza a un informe web interactivo con todos los patrones visualizados.

El result.summary ofrece una descripción narrativa generada por LLM:

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.",
#  ...]

Cómo funciona

Disco es un pipeline, no ingeniería de prompts sobre datos. Hace lo siguiente:

  1. Entrena modelos de aprendizaje automático en un subconjunto de tus datos.

  2. Utiliza técnicas de interpretabilidad para extraer patrones aprendidos.

  3. Valida cada patrón en los datos retenidos con corrección FDR (Benjamini-Hochberg).

  4. Compara los patrones supervivientes con la literatura académica mediante búsqueda semántica.

No puedes replicar esto escribiendo código pandas o pidiendo a un LLM que mire un CSV. Encuentra estructuras que el análisis basado en hipótesis pasa por alto porque no comienza con hipótesis.


Preparación de tus datos

Antes de ejecutar, excluye las columnas que producirían hallazgos sin sentido. Disco encuentra patrones estadísticamente reales, pero si la entrada incluye columnas que están relacionadas por definición con el objetivo, los patrones serán tautológicos.

Excluye:

  1. Identificadores: IDs de fila, UUIDs, IDs de paciente, códigos de muestra.

  2. Fuga de datos: el objetivo renombrado o reformateado (p. ej., diagnosis_text cuando el objetivo es diagnosis_code).

  3. Columnas tautológicas: codificaciones alternativas del mismo constructo que el objetivo. Si el objetivo es serious, entonces serious_outcome, not_serious, death son todas parte de la misma clasificación. Si el objetivo es profit, entonces revenue y cost juntos lo componen. Si el objetivo es un índice de encuesta, los subelementos son tautológicos.

Guía completa con ejemplos: SKILL.md


Parámetros

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
)

Las ejecuciones públicas son gratuitas, pero los resultados se publican. Establece visibility="private" para datos privados; esto cuesta créditos.


Ejecución asíncrona

Las ejecuciones tardan unos minutos. Para flujos de trabajo de agentes o scripts que realizan otro trabajo en paralelo:

# 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)

Para scripts síncronos y cuadernos Jupyter:

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

Servidor MCP

Disco está disponible como servidor MCP; no requiere instalación local.

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

Herramientas: 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.

→ Archivo de habilidades del agente completo


Precios

Coste

Ejecuciones públicas

Gratis: los resultados y los datos se publican

Ejecuciones privadas

Los créditos varían según el tamaño del archivo y la configuración: usa engine.estimate()

Nivel gratuito

10 créditos/mes, no se requiere tarjeta

Investigador

49 $/mes: 50 créditos

Equipo

199 $/mes: 200 créditos

Créditos

0,10 $ por crédito

Estima antes de ejecutar:

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

La gestión de la cuenta es totalmente programática: adjunta métodos de pago, suscríbete a planes y compra créditos a través del SDK o la API REST. Consulta la Referencia del SDK de Python o SKILL.md.


Formato de datos esperado

Disco espera una tabla plana: columnas para características, filas para muestras.

| patient_id | age | bmi  | smoker | outcome |
|------------|-----|------|--------|---------|
| 001        | 52  | 28.3 | yes    | 1       |
| 002        | 34  | 22.1 | no     | 0       |
| ...        | ... | ...  | ...    | ...     |
  • Una fila por observación: un paciente, una muestra, una transacción, una medición, etc.

  • Una columna por característica: numérico, categórico, fecha/hora o texto libre, todo está bien.

  • Una columna objetivo: el resultado que quieres entender. Debe tener al menos 2 valores distintos.

  • Los valores faltantes están bien: Disco los maneja automáticamente. No elimines filas ni imputes de antemano.

  • No se necesita pivote: si tus datos ya están en una tabla plana, están listos para usar.

Formatos admitidos: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Máximo 5 GB.

No admitido: imágenes, documentos de texto sin formato, JSON anidado/jerárquico, Excel de varias hojas (usa la primera hoja o exporta a CSV).


Comparado con otras herramientas

Objetivo

Herramienta

Estadísticas resumidas, calidad de datos

ydata-profiling, sweetviz

Modelo predictivo

AutoML (auto-sklearn, TPOT, H2O)

Correlaciones rápidas

pandas, seaborn

Responder a una pregunta específica sobre datos

ChatGPT, Claude

Encontrar lo que no sabes que debes buscar

Disco

Disco no es un reemplazo para EDA o AutoML: encuentra los patrones que esas herramientas pasan por alto. Probamos 18 herramientas de análisis de datos en un conjunto de datos con patrones de verdad fundamental conocidos. La mayoría informó resultados incorrectos con confianza. Disco fue el único que encontró todos los patrones.


Enlaces


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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