unflatten_messages

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

With no annotations, the description effectively discloses key behaviors: it only replaces specific markers, leaves unmatched markers intact, and guarantees functional purity (no mutation, no I/O). However, it omits details about potential errors or edge cases.

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 extremely concise, with two sentences that convey purpose and behavior. Every word adds value, and the purpose is front-loaded. No redundancy.

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 straightforward functionality and lack of output schema, the description covers the core operation and constraints. It could mention acceptable input formats or error handling, but it is sufficient for an experienced user.

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%, and the description provides context that mirrors schema descriptions ('flattened messages array', 'extracted array from flatten_messages'). It does not add significant new meaning beyond the schema, so baseline score is appropriate.

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 identifies the tool as restoring a flattened conversation (specific verb 'restore' and resource 'conversation'). It distinguishes itself from siblings like 'flatten_messages' by explicitly stating it reverses the flattening process.

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 after 'flatten_messages' but does not explicitly state when to avoid using this tool or mention alternative tools for similar tasks. The guidance is clear but not comprehensive.

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