collect_messages

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that embeddings are done best-effort, re-running repairs missing vectors, and undecryptable messages are skipped (not fabricated). It also mentions the autonomous background loop, providing comprehensive behavioral insight.

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 a single, coherent paragraph that is concise yet informative. It front-loads the purpose and then adds details, but could benefit from slight structural improvements (e.g., bullet points) for even easier scanning.

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 tool's simplicity (2 optional parameters, no output schema), the description covers all necessary aspects: purpose, when to use, behavior (best-effort embedding, repair, error handling), and parameter defaults. It is sufficiently complete.

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 both parameters. The description restates the parameter roles and defaults but adds no new semantic information beyond what is 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 tool's purpose: bulk-fetching recent messages from LINE conversations into a cross-conversation search index. It uses specific verbs ('bulk-fetch') and resources ('recent messages'), and distinguishes itself from siblings by noting LINE's lack of native cross-chat search and the tool's role in indexing.

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 specifies that the tool should only be called to force a reconcile or backfill specific chats, as a background capture loop normally keeps the index current. This provides clear usage context, though it could explicitly reference sibling tools like get_chat_messages for comparison.

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