AI-Collab-Memory

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool loads saved checkpoints for resuming work, it doesn't describe what 'loading' entails operationally - whether it restores state, overwrites current work, requires specific permissions, or has any side effects. For a tool that presumably modifies system state, this lack of behavioral detail is a significant gap.

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 perfectly concise with just two sentences. The first sentence states the core functionality, and the second provides usage context. Every word earns its place with no redundancy or unnecessary elaboration. The structure is front-loaded with the primary purpose stated first.

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 has 2 parameters with full schema coverage but no annotations and no output schema, the description provides adequate but minimal context. It explains what the tool does and when to use it, but doesn't address behavioral aspects, return values, or error conditions. For a checkpoint loading tool that likely affects system state, more completeness would be beneficial, but it meets minimum viable standards.

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 schema has 100% description coverage, with both parameters clearly documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.

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 action ('불러옵니다' - loads/retrieves) and resource ('저장된 체크포인트' - saved checkpoints), making the purpose understandable. It distinguishes from siblings like 'save_checkpoint' by focusing on retrieval rather than creation. However, it doesn't explicitly differentiate from other retrieval tools like 'get_work_log' or 'search_research' beyond the checkpoint focus.

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 clear context for when to use this tool: '중단된 작업을 이어서 시작할 때' (when resuming interrupted work). This gives practical guidance about the tool's intended scenario. However, it doesn't specify when NOT to use it or mention alternatives among sibling tools, which prevents a perfect score.

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