perf_data_convert

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

No annotations are provided, so the description must disclose behavior. It explains the mutual exclusivity of output formats and the output path return. However, it doesn't mention that the tool writes output files, whether it overwrites existing files, or any side effects, which are relevant for a conversion tool.

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 and well-structured, with a clear opening sentence, a use-case line, and bullet points for key parameters. Every sentence adds value, and the output line is informative.

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 (8 params) and presence of an output schema, the description covers the essential context: its purpose, the key parameters, the constraint, and the return value. It is complete for an AI agent to select and invoke the tool 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 100%, so baseline is 3. The description adds meaning by highlighting key parameters (to_json, to_ctf, all, tod) and providing an example path. It also introduces the exactly-one rule, which is not encoded in 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?

Description clearly states the tool converts perf.data to JSON or CTF, with a specific verb and target formats. This distinguishes it from sibling tools like perf_report or perf_annotate, which analyze rather than export data.

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?

Explicitly states the use case: 'Use this to export data for processing in external tools.' It also provides a constraint (exactly one of to_json or to_ctf). It doesn't mention when-not-to-use or alternative tools, 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.