AI-Collab-Memory

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the persistence aspect ('저장합니다' - saves) and future searchability, but doesn't address critical behavioral traits like whether this operation is idempotent, what permissions are required, whether it overwrites existing research, or what happens on failure. For a write operation with zero annotation coverage, this is inadequate.

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 appropriately concise with two sentences that each serve a purpose: the first states the core action, the second explains the value/context. There's no wasted text, though it could be slightly more structured by front-loading the most critical information.

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 this is a write operation with 5 parameters, no annotations, and no output schema, the description should do more to explain behavioral aspects. While it covers the basic purpose and persistence context, it lacks information about what the tool returns, error conditions, or how it differs from similar tools like 'save_checkpoint'. The description is minimally adequate but has clear gaps.

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%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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 with a specific verb ('저장합니다' - saves) and resource ('리서치 결과' - research results), and distinguishes it from siblings by mentioning future searchability. However, it doesn't explicitly differentiate from 'save_checkpoint' or 'log_work' which might have overlapping functionality.

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 context ('다른 AI가 나중에 검색해서 활용할 수 있습니다' - other AIs can search and utilize it later), suggesting this is for persistent storage of research findings. However, it doesn't provide explicit when-to-use guidance or alternatives, nor does it mention when NOT to use it versus siblings like 'save_checkpoint' or 'log_work'.

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