One inbox for comments, replies, mentions, reviews and DMs

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

Annotations provide readOnlyHint=true and destructiveHint=false, which the description aligns with. Beyond that, it discloses critical behaviors: unconnected channels are silently skipped, failures are named in `notes`, DMs fold to one item per conversation, and X incurs per-API-call costs. This adds substantial context beyond annotations, especially around failure handling and billing, which is essential for correct use. No contradiction with annotations.

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 lengthy but densely packed with essential information, each sentence earning its place. It is front-loaded with the tool's core function, then usage guidance, then critical behavioral details and pricing. While it is verbose, the structure is logical and avoids fluff, though a slightly more compact version could be imagined without losing value.

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 tool with no output schema and high complexity (multiple channels, edge cases), the description is remarkably complete. It covers what happens on unconnected channels, failures, DM consolidation, and cost implications for X. It also references how to obtain conversationId for DMs. An agent has all the necessary context to call it correctly and interpret results, including the `notes` field behavior.

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 100%, so the baseline is 3. The description adds value by explaining how conversationId is used (to include DMs and its origin sources) and the DM folding behavior, which clarifies the parameter's role. It also implicitly ties the `sources` parameter to the channel names listed, adding semantic richness beyond the schema's enum-like list.

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 states a clear purpose: aggregating all comments, replies, mentions, reviews, and DMs across multiple channels into one list. It enumerates the specific channel types (Facebook, Instagram, Threads, YouTube, Reddit, Google Business, Bluesky, X, Meta DMs, Telegram) and distinctly positions itself as the unified 'inbox' compared to per-channel list tools like list_meta_comments or list_x_dms. It also ties to the reply_to_inbox_item sibling, clarifying its role in the workflow.

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 states when to use it: "Use this for 'what do I need to reply to', 'any new comments', 'any new DMs', 'how are people responding'." It also contrasts with per-channel tools by saying "every other source only re-reads what the per-channel tools already read," implying this is the aggregated alternative. It does not explicitly say when NOT to use it (e.g., when you need deep per-channel analytics), but the context is clear enough for an agent to choose this for a unified view.

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