Disco

com.leap-labsPublished by leap-labs.comOfficial publisherv1.0.1Updated Oct 24, 2026

Find novel, statistically validated patterns in tabular data — hypothesis-free.

VerifiedStreamable HTTPWeb executableFinanceData & Analytics

Overview

AI-generated overview

Disco is a remote MCP server that runs a statistical discovery pipeline on tabular data to surface validated, literature-checked patterns an assistant can then,

What you can do
Upload tabular datasets by URL, path or content and run a discovery analysis against a chosen target column, then poll status and fetch patterns with conditions, p-values, novelty scores and citations. Estimate credit cost before a run and list available plans and pricing. Check account plan, credits and payment method status. Sign up or log in by email verification code to obtain an API key. Attach a Stripe payment method, buy credit packs and change subscription plan.
When to use it
Useful when someone wants to know which variables matter for an outcome, suspects there are interactions or subgroup effects they have not thought to test, or does not know where to start with a flat dataset. Not intended for summary statistics, charts, filtering or SQL, which other tools handle.
Requirements
A remote streamable HTTP endpoint; no local install. A Disco API key sent as an Authorization bearer token (format disco_...), obtainable through the signup or login tools or the developer page. Network access to the Disco service. Private analyses consume credits; the free tier includes 10 credits per month.
Before you install
Private analyses cost credits, and the server can attach a payment method, purchase credit packs at $0.10 per credit and start or change paid subscriptions ($49 or $199 per month), which spends money. Public runs are free but the data and report are published, so private data needs the paid private mode. Uploaded datasets leave the machine for a third-party service, and the API key is a credential. Signup and login send email verification codes.

Installation

In SourceWeft

  1. Open Disco in the dashboard and add it to a workspace.
  2. Connect Authorization token credentials. They are stored privately and never shown on public pages.
  3. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/com.leap-labs%2Fdiscovery-engine/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "disco": {
      "type": "http",
      "url": "https://disco.leap-labs.com/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}

README

Disco

Find novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.

[PyPI] [License: MIT]

Made by Leap Laboratories.

What it actually does

Most data analysis starts with a question. Disco starts with the data.

Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like "patients aged 45–65 with low HDL and high CRP have 3× the readmission rate" — without you needing to hypothesise that interaction first.

Each pattern is:

  • Validated on a hold-out set — increases the chance of generalisation
  • FDR-corrected — p-values included, adjusted for multiple testing
  • Checked against academic literature — to help you understand what you've found, and identify if it is novel.

The output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.

Use it when: "which variables are most important with respect to X", "are there patterns we're missing?", "I don't know where to start with this data", "I need to understand how A and B affect C".

Not for: summary statistics, visualisation, filtering, SQL queries — use pandas for those

Quickstart

bash
pip install discovery-engine-api

Get an API key:

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

Or create a key at disco.leap-labs.com/developers.

Run your first analysis:

python
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}")

Runs take a few minutes. discover() polls automatically and logs progress — queue position, estimated wait, current pipeline step, and ETA. For background runs, see Running asynchronously.

→ Full Python SDK reference · Example notebook

What you get back

Each Pattern in result.patterns looks like this (real output from a crop yield dataset):

python
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,)

Key things to notice:

  • Patterns are combinations of conditions — humidity AND wind speed together, not just "more humidity is better"
  • Specific thresholds — 72–89%, not a vague correlation
  • Novel vs confirmatory — every pattern is classified; confirmatory ones validate known science, novel ones are what you came for
  • Citations — shows what IS known, so you can see what's genuinely new
  • report_url links to an interactive web report with all patterns visualised

The result.summary gives an LLM-generated narrative overview:

python
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.",#  ...]

How it works

Disco is a pipeline, not prompt engineering over data. It:

  1. Trains machine learning models on a subset of your data
  2. Uses interpretability techniques to extract learned patterns
  3. Validates every pattern on the held-out data with FDR correction (Benjamini-Hochberg)
  4. Checks surviving patterns against academic literature via semantic search

You cannot replicate this by writing pandas code or asking an LLM to look at a CSV. It finds structure that hypothesis-driven analysis misses because it doesn't start with hypotheses.

Preparing your data

Before running, exclude columns that would produce meaningless findings. Disco finds statistically real patterns — but if the input includes columns that are definitionally related to the target, the patterns will be tautological.

Exclude:

  1. Identifiers — row IDs, UUIDs, patient IDs, sample codes
  2. Data leakage — the target renamed or reformatted (e.g., diagnosis_text when the target is diagnosis_code)
  3. Tautological columns — alternative encodings of the same construct as the target. If target is serious, then serious_outcome, not_serious, death are all part of the same classification. If target is profit, then revenue and cost together compose it. If target is a survey index, the sub-items are tautological.

Full guidance with examples: SKILL.md

Parameters

python
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)

Public runs are free but results are published. Set visibility="private" for private data — this costs credits.

Running asynchronously

Runs take a few minutes. For agent workflows or scripts that do other work in parallel:

python
# Submit without waitingrun = 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)

For synchronous scripts and Jupyter notebooks:

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

MCP server

Disco is available as an MCP server — no local install required.

json
{  "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.

→ Full agent skill file

Pricing

Cost
Public runsFree — results and data are published
Private runsCredits vary by file size and configuration — use engine.estimate()
Free tier10 credits/month, no card required
Researcher$49/month — 500 credits
Team$199/month — 2000 credits
Credits$0.10 per credit

Estimate before running:

python
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

Account management is fully programmatic — attach payment methods, subscribe to plans, and purchase credits via the SDK or REST API. See Python SDK reference or SKILL.md.

Expected data format

Disco expects a flat table — columns for features, rows for samples.

| patient_id | age | bmi  | smoker | outcome ||------------|-----|------|--------|---------|| 001        | 52  | 28.3 | yes    | 1       || 002        | 34  | 22.1 | no     | 0       || ...        | ... | ...  | ...    | ...     |
  • One row per observation — a patient, a sample, a transaction, a measurement, etc.
  • One column per feature — numeric, categorical, datetime, or free text are all fine
  • One target column — the outcome you want to understand. Must have at least 2 distinct values.
  • Missing values are OK — Disco handles them automatically. Don't drop rows or impute beforehand.
  • No pivoting needed — if your data is already in a flat table, it's ready to go

Supported formats: CSV, TSV, Excel (.xlsx), JSON, Parquet, ARFF, Feather. Max 5 GB.

Not supported: images, raw text documents, nested/hierarchical JSON, multi-sheet Excel (use the first sheet or export to CSV)

Compared to other tools

GoalTool
Summary statistics, data qualityydata-profiling, sweetviz
Predictive modelAutoML (auto-sklearn, TPOT, H2O)
Quick correlationspandas, seaborn
Answer a specific question about dataChatGPT, Claude
Find what you don't know to look forDisco

Disco isn't a replacement for EDA or AutoML — it finds the patterns those tools miss. We tested 18 data analysis tools on a dataset with known ground-truth patterns. Most confidently reported wrong results. Disco was the only one that found every pattern.

Links

Source: README.md at commit 70cc9b9

Tools

14

discovery_list_plans

List Plans

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.

Takes no parameters.

discovery_estimate

Estimate Analysis Cost

Estimate the credits required to run a Disco analysis. Returns `required_credits` for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes `account.available_credits`.

Parametersapi_keynum_columnsrequiredfile_size_mbrequiredanalysis_depth

discovery_upload

Upload Dataset

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.

Parametersapi_keyfile_urlfile_namefile_pathfile_content

discovery_analyze

Run Discovery Analysis

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

Parameterstitleauthorapi_keyfile_refuse_llmssource_urlvisibilitydescriptiontarget_columnrequiredanalysis_depthexcluded_columnscolumn_descriptions

discovery_status

Check Analysis Status

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_wait_seconds: estimated queue wait time in seconds (pending only) Poll this after calling discovery_analyze. 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.

Parametersrun_idrequiredapi_key

discovery_get_results

Get Analysis Results

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 visualizations - **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.

Parametersrun_idrequiredapi_key

discovery_account

Get Account Info

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.

Parametersapi_key

discovery_signup

Sign Up

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).

Parametersnameemailrequired

discovery_signup_verify

Verify Sign Up

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.

Parameterscoderequiredemailrequired

discovery_login

Log In

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.

Parametersemailrequired

discovery_login_verify

Verify Log In

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.

Parameterscoderequiredemailrequired

discovery_add_payment_method

Add Payment Method

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.

Parametersapi_keypayment_method_idrequired

discovery_purchase_credits

Purchase Credits

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.

Parameterspacksapi_key

discovery_subscribe

Subscribe to Plan

Subscribe to or change your Disco plan. Available plans: - "free_tier": Explorer — free, 10 credits/month - "tier_1": Researcher — $49/month, 500 credits/month - "tier_2": Team — $199/month, 2000 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.

Parametersplanrequiredapi_key

Version history

1
  1. v1.0.1LatestSep 16, 2026

Score

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  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
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  • Documentation15 pts
  • License10 pts