AI-Collab-Memory

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions the tool 'records' work, implying a write/mutation operation, but doesn't specify whether this creates new entries, updates existing ones, requires permissions, has side effects, or what happens on failure. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps unexplained.

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 (two sentences) and front-loaded with the core purpose first, followed by use cases. Every sentence adds value: the first states what the tool does, and the second explains why it's useful. There's zero redundancy or wasted words, making it highly efficient for an AI agent to parse.

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 moderate complexity (5 parameters, mutation operation) and lack of both annotations and output schema, the description is minimally adequate. It covers the purpose and use cases but lacks details on behavioral aspects like error handling, persistence model, or return values. The 100% schema coverage helps, but for a write tool with no structured safety hints, more context would be beneficial.

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 description coverage is 100%, with all 5 parameters well-documented in the schema (agent, action, detail, result, tags). The description doesn't add any parameter-specific information beyond what the schema provides, such as format examples or constraints. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding but doesn't need to compensate for gaps.

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 tool's purpose: 'AI가 수행한 작업을 기록합니다' (records work performed by AI). It specifies the verb ('기록합니다' - records) and resource ('작업' - work/tasks). While it doesn't explicitly differentiate from all siblings, it does mention use cases like tracking '누가 뭘 했는지' (who did what) and resuming interrupted work, which helps distinguish it from purely retrieval tools like get_work_log.

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 implied usage context by mentioning when the recorded information is useful ('나중에 확인하거나 중단된 작업을 이어서 할 때' - for later review or resuming interrupted work). However, it doesn't explicitly state when to use this tool versus alternatives like save_checkpoint or save_research, nor does it provide exclusion criteria. The guidance is helpful but incomplete for sibling differentiation.

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