AI-Collab-Memory

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

No annotations are provided, so the description carries full burden. It mentions filtering capabilities but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, how results are ordered, pagination behavior beyond the 'limit' parameter, or what format/logs are returned. For a query tool with zero annotation coverage, this leaves significant gaps.

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 with two sentences that directly address purpose and filtering capabilities. It's front-loaded with the main action and avoids unnecessary details. However, it could be slightly more structured by explicitly separating purpose from parameter guidance.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what '작업 로그' contains, the return format, error conditions, or behavioral traits like read-only nature. For a tool with 3 parameters and query functionality, more context is needed to guide effective use.

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 three parameters with descriptions. The description adds marginal value by mentioning filtering by '특정 AI' (specific AI) and '태그' (tag), which aligns with the 'agent' and 'tag' parameters, but doesn't provide additional syntax, format examples, or constraints beyond what's 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?

The description clearly states the verb '조회합니다' (retrieve/query) and resource '작업 로그' (work logs), making the purpose understandable. It distinguishes from siblings like 'log_work' (which creates logs) and 'search_research' (which searches research content). However, it doesn't specify whether this retrieves all logs or has implicit limitations beyond filtering.

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 by mentioning filtering capabilities ('특정 AI의 작업이나 태그로 필터링할 수 있습니다'), suggesting when to use these parameters. However, it doesn't explicitly state when to choose this tool over alternatives like 'search_research' or provide exclusion criteria (e.g., when not to use it).

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