discover_petri_net

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

With no annotations, the description discloses algorithmic behavior (three miners) and return format (net, marking, counts) but does not state side effects (e.g., read-only, persistence) or permissions. Provides moderate transparency.

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, well-structured with a clear first-line purpose, bulleted algorithm details, and a final sentence on output. No unnecessary words; front-loaded with key information.

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 an output schema exists, the description sufficiently covers the return value and algorithmic details. However, it does not mention that log_id must refer to an existing event log (precondition), leaving a minor gap.

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?

Despite 0% schema description coverage, the description adds meaning to all three parameters: log_id is implied, algorithm is explained with behavior, and noise_threshold is constrained to [0,1] for inductive. Effectively compensates for schema gap.

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 discovers a Petri net from an event log, specifies three algorithms with distinct characteristics, and distinguishes from sibling discovery tools (e.g., discover_dfg, discover_process_tree) by focusing on Petri net output.

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 explains algorithm options and parameters but lacks explicit guidance on when to choose one algorithm over another or when to use this tool vs alternatives like discover_process_tree. Usage context is implied but not clearly directed.

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