DAG Studio MCP

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Works with Claude Code, VS Code, Cursor, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install connector/blackswancausallabs-com-dagstudio-mcp

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What this server can do and what it asks for, from its tool list, install config and code. Checked Nov 1, 2026.

Tools

6 read · 0 write · 0 delete · 3 not yet classified

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1 (API keys, tokens or passwords)

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Tools (9)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• analyze_dag

  Returns identifiability status and minimal adjustment sets given the DAG provided. Specifically: open backdoor paths from exposure to outcome (Pearl 2009 Theorem 3.3.2), the minimal sufficient adjustment sets that block them, and all directed paths. Accepts either a canonical DAG object or a `dagitty_string`. Returns a diagnostics block summarizing identifiability, unmeasured confounding, and any flagged issues, plus a static concordance attestation for the engine release. DAG Studio verifies analyses given a DAG. It does not verify that the DAG correctly encodes domain knowledge or that the…

• check_overadjustment

  Given a DAG and a proposed adjustment set, identify variables whose inclusion biases the estimate. Detects three failure modes: • descendant_of_exposure — variable is caused by the exposure; conditioning on it blocks part of the causal effect being estimated, changing the estimand (Schisterman 2009). • collider — variable is a collider on a non-causal X→Y path whose conditioning opens that path; conditioning is unjustified unless another set member re-blocks it. • descendant_of_collider — variable is downstream of an unconditioned collider; conditioning induces the same M-bias as conditi…

• classify_effect_modification

  For each modifier annotation on the DAG, classify the effect-modification structure per VanderWeele & Robins (2007) and Weinberg (2007). The five types and their regulatory implications: • direct — modifier is structurally independent of the E-D system; subgroup-specific effects are interpretable as pure modification. • indirect — modifier acts through a mediator; subgroup effects conflate modification with mediation. • common-cause — modifier shares an ancestor with the exposure; subgroup effects are confounded unless the ancestor is also adjusted. • proxy — modifier is unobserved; su…

• compute_bias

  Numerically demonstrate the bias of a proposed adjustment set. Computes the true total effect analytically from the SEM edge coefficients (sum of products along all directed X→Y paths), then fits two OLS regressions on simulated data: crude (Y ~ X) and adjusted (Y ~ X + Z). Reports each estimate, each bias against the true effect, and the bias reduction |crude_bias| − |adjusted_bias|. Translates a structural claim ("adjust for {age, smoking}") into a numerical demonstration. Composes check_overadjustment internally — any overadjustment flags emitted there are surfaced in this tool's diagnosti…

• generate_code

  Emit idiomatic Python (networkx.DiGraph) or R (dagitty DSL) source representing the DAG. The identifier_map lets agents trace canvas labels to sanitized code identifiers when these differ (e.g., when labels contain spaces or special characters that aren't valid Python/R identifiers). Useful for handing the DAG to a downstream analysis pipeline. Also returns dagstudio_url — a one-click deep-link that opens the same DAG on the DAG Studio canvas at dagstudio.blackswancausallabs.com. Surface this URL to the user alongside the generated code so they can paste-free open the DAG visually, regardless …

• get_canonical_example

  Return one of the canonical validated DAGs from the engine's test suite by ID (T01–T15 for backdoor / adjustment-set canonical structures, EM01–EM20 for effect-modification structures from VanderWeele-Robins 2007 and Weinberg 2007). Useful for few-shot prompting, regression checks, and teaching. The returned DAG is the same one the engine is validated against, so analyze_dag's output on it will match the listed expected values.

• parse_dagitty

  Parse a dagitty DSL string into the canonical DAG shape used by other DAG Studio tools. Accepts dagitty('dag { ... }'), dagitty("..."), and dagitty::dagitty(...) forms. Bidirected (<->) and undirected (--) edges are skipped — DAG Studio represents only directed edges. This tool parses notation; it does not validate that the encoded DAG is a correct or complete causal model. That is a domain-knowledge question outside the scope of any graph tool. When the DAG depicts a paper's causal model, encode the structural assumptions of the study being protocolized, including those that are implicit in…

• simulate_data

  Generate synthetic data from a Linear Gaussian Structural Equation Model consistent with the DAG. Root variables are sampled N(0,1); each non-root is a linear combination of its parents (default β = 0.5 per edge, overridable via `coefficients`) plus N(0, 0.25) noise. Output is deterministic given seed (default 42). Useful for sensitivity analysis and for demonstrating structural claims (e.g., 'adjustment for {age, smoking} eliminates the confounding shown by these data'). Outputs are consistent with the DAG under the linear Gaussian model only — real datasets generated under unknown processes…

• validate_engine

  Run the canonical validation suite (T01–T15 backdoor / adjustment-set cases, EM01–EM20 effect-modification structures from VanderWeele-Robins 2007 and Weinberg 2007) against the current engine and return pass/fail per case. Use this when an agent or reviewer wants to verify the engine is trustworthy before relying on analyze_dag, check_overadjustment, or classify_effect_modification.

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Last commit

Sep 28, 2026 (1 month ago)

Last release

Jul 20, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Registry name

com.blackswancausallabs/dagstudio-mcp

Version

0.1.0

Auth

Bearer token

License

Apache-2.0

Repository

github.com/Black-Swan-Causal-Labs/dagstudio-mcp

Website

dagstudio.blackswancausallabs.com

First seen

Oct 24, 2026

Last checked

Nov 1, 2026

Source

Official MCP Registrygithub-graphql