simulate_log

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

With no annotations provided, the description includes key behavioral details: the returned log_id is of kind 'log' and composes with Phase 1 tools, source_handle tracks lineage, and num_traces is capped at 10,000 to prevent runaway generation. It does not cover potential errors or performance, but the provided info is sufficient.

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?

The description is concise, with no wasted words. It immediately states the purpose, then provides model-type constraints, behavioral details, and a limit on traces. Each sentence serves a clear purpose.

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?

Given the tool's complexity (2 parameters, output schema exists) and no annotations, the description covers inputs, conversion requirements, output format, and constraints. It is self-contained and integrates well with sibling tools like convert_model and other discovery tools.

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?

Schema coverage is 0%, but the description explains model_id as a handle from a discovered Petri net or process tree, and num_traces with a default and cap. This adds significant meaning beyond the bare schema, though it could be more explicit about the source of valid handles.

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 clearly states the tool simulates an event log by replaying a discovered model, specifying it accepts Petri net and process tree handles. This distinguishes it from siblings like abstract_* and conformance_* tools, which focus on analysis or checking, not simulation.

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 gives explicit guidance on supported model types (Petri net, process tree) and unsupported ones (BPMN, POWL) with a conversion workaround via convert_model. It also mentions the num_traces cap. It lacks explicit 'when not to use' statements, but the context is clear.

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