conformance_token_replay

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

No annotations are provided, so description carries the burden. It discloses that the response is compact and returns only mean fitness and count, and that detailed diagnostics require a different approach. This sets clear expectations for behavior.

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 three sentences, front-loading the purpose. Every sentence adds value, with no redundancy or fluff.

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 and lack of annotations, the description adequately explains the return values (mean fitness, count) and notes the compact response. It does not cover prerequisites or error cases, but for a technical conformance tool, it provides sufficient context to use it correctly.

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% and the description does not explain the two parameters (log_id, petri_id). It relies on domain knowledge, which may be insufficient for an AI agent unfamiliar with the context. The description adds no semantic value 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 clearly specifies the tool performs a token-based replay conformance check, returning mean trace fitness and count of perfectly-fit traces. It distinguishes from sibling conformance_alignments by naming the method (token replay).

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 implies usage for conformance checking and mentions that for detailed per-trace diagnostics, users should use PM4Py directly, indicating when to use this tool vs a more detailed alternative. However, it does not explicitly state when to prefer token replay over other conformance methods like alignments.

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