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dag-studio-mcp

dag-studio-mcp

Official
by Black-Swan-Causal-Labs
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JavaScript
Hybrid

validate_engine

Validate the causal-inference engine against canonical test cases (T01-T15 backdoor/adjustment, EM01-EM20 effect modification) and get pass/fail results.

Instructions

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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "validate_engine"
  }
}

Input Schema

NameRequiredDescriptionDefault
suiteNo'main' runs T01–T15. 'effect_modification' runs EM01–EM20. 'all' runs both. Default: 'all'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses that the tool runs specific validation suites (T01–T15, EM01–EM20) and returns pass/fail per case, which is the core behavioral expectation. It does not explicitly state whether it has side effects, but 'run... and return' implies a read-only verification operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main action and return type, then a clear usage sentence. Every clause provides useful information—case IDs, references, intended use—and no words are wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity tool with one optional parameter and no output schema, the description adequately specifies the return behavior (pass/fail per case) and the suite contents. It also ties the tool into the larger toolset, making the context complete for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the single parameter 'suite' with enum values and descriptions. The tool description adds contextual references to the canonical cases but does not materially enhance parameter semantics beyond what the schema already provides, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: 'Run the canonical validation suite... against the current engine and return pass/fail per case.' It clearly identifies what the tool does and distinguishes it from siblings by naming analyze_dag, check_overadjustment, and classify_effect_modification as tools to verify before relying on.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use it: 'Use this when an agent or reviewer wants to verify the engine is trustworthy before relying on...' It names the specific sibling tools as downstream consumers. It does not provide explicit when-not-to-use guidance, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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