simulate_data

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

With no annotations, the description burdens itself well by disclosing the full generative process: root variables N(0,1), non-roots with β=0.5 and N(0,0.25) noise, and determinism via seed. It also clearly states the limitation that real data won't match the distribution, which is critical context.

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

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

Two tightly structured paragraphs: the first explains the model and parameters, the second gives use cases and limitations. No filler; every sentence earns its place and the core purpose is front-loaded.

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

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

The description covers statistical model, determinism, use cases, and a critical validity limitation, making it quite complete for a simulation tool. However, it does not specify the output shape or the expected DAG input format, which an agent might need to invoke successfully.

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

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

The schema covers 80% of parameters, leaving dag without a description. The description compensates by explaining that the DAG defines the structural model and that outputs are consistent with it. It also enriches coefficients and seed semantics beyond the schema.

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

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

The description opens with 'Generate synthetic data from a Linear Gaussian Structural Equation Model consistent with the DAG,' clearly identifying the verb, output, and model type. This directly distinguishes it from analysis/validation siblings like analyze_dag and check_overadjustment.

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

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

The description explicitly states it is useful for sensitivity analysis and demonstrating structural claims, with a concrete example. It also warns that outputs are only valid under the linear Gaussian model, implying when not to use it, though it does not explicitly name alternative tools.

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