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DAG Studio MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/21/2026, 9:03:03 PM

DAG Studio MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time — check back soon.
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Causal DAG analysis: backdoor paths, adjustment sets, bias simulation. Validated against dagitty.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON â–¾

Client Config & Setup

Remote HTTP
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "dag-studio-mcp": {
      "url": "https://dagstudio-mcp.blackswancausallabs.com/mcp"
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (9) Directory Badge Claim listing Alternatives🧮 More in Data Science Tools

Capabilities & Tool Schemas (9) ~195 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server — may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by DAG Studio MCP.

analyze_dag

Backdoor paths, minimal sufficient adjustment sets, identifiability

parse_dagitty

Parse dagitty DSL (raw or R-wrapped) into the structured DAG model

generate_code

R / Python analysis code for a DAG, plus a one-click DAG Studio URL

check_overadjustment

Detect adjustment for mediators, colliders, and descendants of exposure

simulate_data

Simulate data from a DAG under user-specified structural coefficients

compute_bias

Empirical bias of an adjustment strategy against the simulated truth

Documentation Overview

dag-studio-mcp

CI License: Apache 2.0

Model Context Protocol (MCP) server for the DAG Studio causal-inference engine. It lets AI agents construct, analyze, and validate causal directed acyclic graphs (DAGs) using the same engine that powers the DAG Studio canvas.

Built and maintained by Black Swan Causal Labs. Listed in the RWE MCP Registry.

Tools

ToolWhat it does
analyze_dagBackdoor paths, minimal sufficient adjustment sets, identifiability
parse_dagittyParse dagitty DSL (raw or R-wrapped) into the structured DAG model
generate_codeR / Python analysis code for a DAG, plus a one-click DAG Studio URL
check_overadjustmentDetect adjustment for mediators, colliders, and descendants of exposure
simulate_dataSimulate data from a DAG under user-specified structural coefficients
compute_biasEmpirical bias of an adjustment strategy against the simulated truth
classify_effect_modificationClassify effect-modifier structure (direct, indirect, proxy, common-cause, pure interaction)
get_canonical_exampleCanonical teaching DAGs (confounding, M-bias, frontdoor, and others)
validate_engineRun the full canonical validation suite and report engine version

Every analytical response carries an engine_version stamp, a concordance attestation, a diagnostics block with severity-coded flags, and citations to the underlying methods literature.

Validation

The engine is validated four ways for coherence: against Pearl (2009) theory, against the reference implementation dagitty (Textor et al. 2016), against DAG Studio's own analytical results, and empirically via compute_bias on simulated data.

  • 35 canonical cases: T01 to T15 (structural identification) and EM01 to EM20 (effect modification), runnable live via validate_engine.
  • A release-gate concordance check runs the engine head-to-head against dagitty (vendored at upstream commit 7a65777) and stamps the attestation surfaced in tool responses.
  • 93 unit and integration tests across the engine bindings, the tool layer, and the auth gate.

Hosted endpoint

The server runs as a Cloudflare Worker (Streamable HTTP):

Code
https://dagstudio-mcp.blackswancausallabs.com/mcp

Access is token-gated during the trial period. Request a token at jdiazdecaro@blackswancausallabs.com. Tokens are accepted either as a bearer header or as a ?token= query parameter (the query form exists for clients whose connector UI cannot set custom headers, such as the Claude.ai web connector).

Claude Code:

Terminal
claude mcp add --transport http dag-studio \
  https://dagstudio-mcp.blackswancausallabs.com/mcp \
  --header "Authorization: Bearer <your token>"

Claude.ai web: add a custom connector pointed at https://dagstudio-mcp.blackswancausallabs.com/mcp?token=<your token>.

Repository layout

  • dag-engine.js / dag-engine.d.ts: the analytical engine, a pure ESM module with no runtime dependencies
  • src/tools/: one file per tool, each exporting { InputSchema, OutputSchema, descriptor, handler }
  • src/worker/: Cloudflare Worker transport and the token gate (auth.ts)
  • src/index.ts: stdio entry point for local use
  • ci/: release-gate concordance harness against vendored dagitty
  • tests/: unit and integration tests (npm test)
  • MCP_REQUIREMENTS.md: the v1 specification
  • FDA_GUIDANCE_ALIGNMENT.md: mapping of DAG Studio capabilities onto FDA draft RWE guidance protocol elements

The engine is developed alongside the DAG Studio canvas app and the vendored copy here is synced at release time. This repository is self-contained: clone it, npm install, and everything builds and tests without further setup.

Development

Terminal
npm install
npm test                  # full suite
npm run dev               # stdio server via tsx
npm run worker:typecheck  # worker bundle typecheck
npm run worker:deploy     # deploy (stamps engine_version from git HEAD first)

For interactive inspection: npx @modelcontextprotocol/inspector.

Protocol status

Built on @modelcontextprotocol/sdk (TypeScript). Current against the finalized MCP specification revision 2025-11-25. Migration to the 2026-07-28 revision is planned once stable SDK support ships.

License

Apache License 2.0 (see LICENSE).

Exception: ci/dagitty-src/ contains the dagitty reference engine (GPL-2.0, Textor et al.), vendored at upstream commit 7a65777 solely as a release-time CI fixture for the concordance check. It retains its own license, is excluded from the published npm package, and is not part of the deployed worker bundle.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads
1.1M
Package downloads in the last 30 days.
Last commit
2mo ago
Most recent push to the default branch.
Tools exposed
9
Callable tools this server registers over MCP.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about DAG Studio MCP

DAG Studio MCP is a hosted MCP server. Add it as a remote server in your client's config: "mcpServers": { "dag-studio-mcp": { "url": "https://dagstudio-mcp.blackswancausallabs.com/mcp" } }

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Technical Specs & Signals

Category🧮Data Science Tools
More technical detailsExpand â–¾
TransportSSE (Remote)
Last updatedJul 20, 2026
15/15 checks healthy over the last 45d
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit2mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Jul 20, 2026
npm downloads1,093,328/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
49Quality signal: Fair · 49/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership6/20
Documentation & tools25/30
Adoption & activity6/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

2 high-severity advisories on record for this package. Most advisories affect transitive dependencies and may not be exploitable in this server's actual usage — this is a directional signal, not a security audit.

Critical 1High 1Medium 0Low 0

Scanned 8/17/2026 via OSV.dev · https://dagstudio-mcp.blackswancausallabs.com/mcp (npm)

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