ask_bookmarks

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

With no annotations, the description carries the full transparency burden. It discloses that the tool uses AI and focuses on saved tweets, which is helpful, but it does not mention any side effects, limitations, or behavior when no relevant bookmarks are found. No contradiction exists, but more detail on the AI-driven behavior could be added.

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 two sentences long, front-loaded with the core action ('Ask a question about your bookmarks'), and contains no filler. Every word adds value, making it highly concise and well-structured.

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?

For a simple one-parameter tool with no output schema, the description adequately explains the purpose and mechanism. It covers the what and how, though it omits details about the response format or edge cases. This is acceptable given the low complexity.

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?

The input schema already documents the single 'question' parameter with 100% coverage, so the description adds no extra semantics beyond what the schema provides. The baseline of 3 applies because the schema fully clarifies the parameter.

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: asking a question about bookmarks and using AI to answer based on relevant saved tweets. This distinguishes it from sibling tools like search_bookmarks, which presumably does keyword filtering rather than semantic Q&A.

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 when a user has a natural-language question about their bookmarks, but it does not explicitly contrast with alternatives or state when not to use it. Since search_bookmarks is a sibling, a brief note distinguishing them would strengthen this dimension.

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