Processes discovered deterministically from an object-centric event log derived from the dated rows of the exports (and any event log they include): one per central object type, with case count and happy-path share. Use get_process for the full flow. On a map larger than one call reads, the list is cut and says so: truncated is true and truncated_note explains.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds valuable behavioral context: processes are 'discovered deterministically', and truncation is disclosed ('truncated is true and truncated_note explains'). This goes beyond the annotations without contradicting them, making the tool's behavior predictable.
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 two sentences, front-loaded with the core purpose and immediately differentiating from get_process. The truncation note is placed at the end, which is logical, and there is no redundant or filler text. Every sentence earns its place.
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 absence of an output schema, the description adequately conveys what the tool returns (case count, happy-path share) and the truncation mechanism. It also references get_process for full flow. While it does not specify the exact JSON structure, the described fields are sufficient for an agent to understand the result. Minor gap: the meaning of 'happy-path share' is not elaborated, but it is likely self-explanatory in context.
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 tool has zero parameters, so the schema trivially covers everything. The description adds no parameter-specific details, but none are needed. According to the rubric, a zero-parameter tool receives a baseline of 4, and there is no gap to compensate for.
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 what the tool does: it lists processes derived from an object-centric event log, one per central object type, with case count and happy-path share. It also explicitly contrasts with get_process ('Use get_process for the full flow'), distinguishing it from a key sibling. The purpose is specific and unambiguous.
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 a clear pointer to an alternative: 'Use get_process for the full flow', implying this tool is for a summary/list rather than full detail. It also discloses truncation behavior. However, it does not explicitly state when to use this tool over other list siblings like list_metrics or list_findings, though the context of processes is implicit.
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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