MCP.Market

Disco MCP server

online · 100% uptime
by leap-labs.com·com.leap-labs/discovery-engine·v1.0.1·7 stars
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Find novel, statistically validated patterns in tabular data — hypothesis-free.

A85/100grade A
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A85/100

full report

Adoption
Growing

7 stars

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Disco tools (14, 4 write)

write = sends, deletes, buys or posts
  • discovery_accountFree

    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.

  • discovery_add_payment_methodFree

    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.

  • discovery_analyzewrite actionFree

    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.

  • discovery_estimateFree

    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`.

  • discovery_get_resultsFree

    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.

  • discovery_list_plansFree

    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.

  • discovery_loginFree

    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.

  • discovery_login_verifyFree

    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.

  • discovery_purchase_creditswrite actionFree

    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.

  • discovery_signupwrite actionFree

    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).

  • discovery_signup_verifyFree

    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.

  • discovery_statusFree

    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.

  • discovery_subscribeFree

    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.

  • discovery_uploadwrite actionFree

    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.

Public scan report

scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it

1 low
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 1672ms20/20
  • Tool poisoning14 tool descriptions checked13/15
  • Auth qualityAPI key sent as a header8/15
  • Maintenancelast push 2 days ago15/15
  • Maintainer identitynamespace and repository owner differ; GitHub account older than a year; website matches verified namespace8/10

Findings (1)

  • lowUnusually long tool description (over 2,000 characters)poison.long-description
    tool discovery_analyze: …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. …
Overall 85/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Disco compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
85/100
higher than 70% of 48,255 servers
Tools
14
the typical server has 7
Tools that change things
29%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote server answers 100%
Response time
1,516 ms
faster than 5% of 30,031 servers
GitHub stars
7
more than 86% of 28,688 servers

What its tools let an agent do: 10 read · 4 change

Install Disco in Claude Code, Cursor or VS Code

claude mcp add --transport http discovery-engine https://disco.leap-labs.com/mcp
Add to Cursor

What the publisher says

From the Disco repository's README, as published. We do not edit it. Read it on GitHub

Disco

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

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

pip install discovery-engine-api

Get an API key:

# 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"}

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

Run your first analysis:

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):

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Disco: common questions

Is Disco MCP server safe?
Yes, by our scan: it is graded A (85/100). Read the Disco safety report
How do I install Disco?
It runs remotely at disco.leap-labs.com. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does Disco need an API key?
Yes. The registry entry asks for Authorization.
Is Disco maintained?
The last commit was 39 days ago (2026-09-25). The latest release is v1.0.1.
Is Disco up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
How does Disco compare with other MCP servers?
safety score 85/100 (higher than 70% of 48,255 servers); tools 14 (the typical server has 7); tools that change things 29% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.