import_conversations

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

With no annotations, the description carries the full burden. It discloses that messages are normalized and stored via the standard persistMemory pipeline, and for connector-v1 format, to use the standard schema. This provides moderate behavioral insight, though it omits side effects, idempotency, or error handling.

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: the first clearly states the action and supported formats, the second adds details about auto-detection and storage. No filler, every sentence is substantive and 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 no output schema and no annotations, the description covers core functionality and formats but lacks details on return values, error conditions, performance, or constraints. It is adequate but leaves gaps for a complex import 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?

All three parameters have schema descriptions (100% coverage), so baseline is 3. The description adds value by explaining auto-detection for the format parameter and linking connector-v1 to docs. This goes beyond the schema's static enum, making it a 4.

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 imports conversation files into memory, listing specific formats (Claude Code JSONL, Claude.ai JSON, ChatGPT JSON, Slack JSON, plaintext, connector-v1). It also mentions auto-detection and the explicit format parameter. This clearly differentiates it from sibling tools which are about other memory operations (e.g., store_memory, search_memory).

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 explains the tool's function but does not provide explicit when-to-use or when-not-to-use guidance relative to alternatives. It mentions auto-detection and explicit format, but no comparison with other import methods. Usage is implied rather than stated.

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