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import_conversations

Import conversation logs from Claude Code, ChatGPT, Slack, plaintext, or connector-v1 JSON into memory. Auto-detects format and normalizes messages for persistent storage.

Instructions

Import a conversation file (Claude Code JSONL, Claude.ai JSON, ChatGPT JSON, Slack JSON, plaintext, or connector-v1 JSON) into memory. Auto-detects format or use explicit format parameter. Messages are normalized and stored via the standard persistMemory pipeline. For connector-v1 format, use the standard ConnectorOutputV1 schema (see docs/connector-spec.md).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeYesTarget scope for imported memories, e.g. 'project:myapp'
formatNoConversation format. Use 'auto' to detect automatically. 'connector-v1' for standard connector output.auto
contentYesRaw file content to import
Behavior4/5

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

With no annotations, the description discloses that messages are normalized and stored via the standard persistMemory pipeline, and that format auto-detection is available. This provides useful process context, though it does not address error handling or side effects, meriting a 4.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the primary purpose and lists formats efficiently. Every sentence earns its place, with no redundant information, earning a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, and the description does not explain return values or error scenarios, which is a notable gap for an import tool. However, the description does cover the input formats and processing pipeline, so for moderate complexity it scores a 3.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides detailed descriptions for all three parameters, achieving 100% coverage. The description adds minimal semantic value beyond the schema, only mentioning the explicit format parameter and connector spec reference, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 and lists supported formats. It is specific about the resource and action, but does not explicitly differentiate from siblings like store_memory or batch_store, so it earns a 4.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for importing conversation files in specific formats but does not explicitly state when to use this tool over alternatives or when not to use it. There are no exclusions or alternative tool mentions, so it scores a 3.

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

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