list_recent_conversations

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

The description discloses the ordering (recency), output format (row headers with fields), and parameters. However, with no annotations provided, it does not fully disclose behavioral traits such as whether this is a read-only operation, any rate limits, or error handling. For a read tool, this is adequate but not comprehensive.

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 and well-structured: a one-sentence summary of purpose, usage guidance, output format preview, and a bullet list of parameters with defaults and examples. Every sentence serves a purpose, and key information is front-loaded.

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 that an output schema exists, the description is fairly complete: it covers purpose, usage cues, parameter details, and output format. It does not discuss potential edge cases (e.g., no results, maximum days) or error handling, but these are minor gaps for a simple list tool.

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 0%, so the description carries the full burden for parameter meaning. It provides clear explanations for all four parameters: 'days' (look-back window, default 2), 'limit' (max conversations, default 20), 'platform' (filter with examples), and 'project' (substring match with examples). This adds significant value beyond the schema alone.

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 that the tool returns conversations ordered by recency, not semantic similarity. It provides a specific verb ('Return') and a resource ('conversations'), and implicitly distinguishes from sibling tools like search_ghost_memory by clarifying it is not semantic search. However, it does not explicitly differentiate from other list tools like list_project_conversations.

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 explicitly advises when to use the tool: 'Use this when the user asks for recent conversations...' with examples including Japanese phrases. It does not specify when not to use it or mention alternatives, but provides clear context for its intended use case.

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