format_messages

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

No annotations are provided, so the description must fully disclose behavioral traits. It only describes the basic operation and does not mention permission requirements, rate limits, side effects, or whether the bot needs to be in the channel. This is insufficient for a tool that collects messages.

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 well-structured with an introductory line and an Args section. It is concise but thorough, with no redundant fluff. Slightly verbose due to the Args repetition, but overall effective.

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 presence of an output schema, the description does not need to detail return values. However, it lacks information about error conditions, prerequisites (e.g., bot membership), and rate limits. With 4 parameters and no annotations, more completeness 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 coverage is 0%, but the description provides detailed explanations for each parameter, including examples and context (e.g., channel_id 예시, channel_name purpose in header, limit default, oldest filter). This adds significant value beyond the schema's titles.

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 function: collecting Slack channel messages and formatting them as text for AI analysis. It distinguishes from siblings like fetch_messages by adding the formatting step.

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 the tool is for AI analysis formatting (AI 분석용 텍스트로 포맷팅) but does not explicitly state when to use it over alternatives like fetch_messages or fetch_threads. No exclusion criteria or when-not-to-use guidance.

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