discover_process_tree

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

With no annotations, the description carries full burden. It states the return value ('a handle to the tree plus its structural shape') but omits details like side effects, error conditions, or prerequisites (e.g., log must be loaded). The core behavior is adequately disclosed.

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 three sentences, no wasted words. It front-loads the main action and provides valuable supplementary info (composition, conversion, return value) efficiently.

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 simplicity (2 parameters, output schema exists), the description covers the main points: algorithm used, output type, and conversion properties. It could mention prerequisite log loading, but overall is complete enough.

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 description coverage is 0%, and the description does not explain any parameters (log_id, noise_threshold). It does not add meaning beyond the input schema, failing to compensate for low coverage.

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's purpose: 'Discover a process tree via the Inductive Miner.' It distinguishes from siblings by specifying the algorithm and output type (process tree vs Petri net, BPMN, etc.). Additional context about composition and conversion reinforces purpose.

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 provides context for when to use this tool by highlighting that process trees compose cleanly and convert to other models. However, it does not explicitly state when to choose this over sibling discovery tools or give exclusions, 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.