import_conversations

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

Discloses key behaviors: auto-detects format, normalizes messages, stores via persistMemory pipeline. References connector-v1 schema docs. No annotations exist, so description carries full burden and meets it well.

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?

Three sentences with no waste. First sentence states purpose and supported formats. Second covers auto-detection. Third adds normalization and connector-v1 details. Front-loaded and efficient.

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 annotations and rich schema, description provides complete behavioral context. Explains pipeline and format specifics. Lacks return value info but no output schema exists, so acceptable.

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% (3 params described). Description adds value by explaining auto-detection behavior and referencing connector-v1 schema beyond schema fields. Instructions about auto vs explicit format and normalization enrich understanding.

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?

Clearly states the tool imports a conversation file into memory. Lists supported formats (Claude Code JSONL, Claude.ai JSON, ChatGPT JSON, Slack JSON, plaintext, connector-v1) and distinguishes itself from siblings like store_memory by focusing on importing existing conversation exports.

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?

Tells when to use the tool: to import conversation files. Mentions auto-detection or explicit format. No direct contrast with siblings, but context signals show many memory tools; the description implies this is for importing external conversation data, which is clear enough.

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