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seed_from_text

Parse text content into individual memories and store them for initial setup or bulk import.

Instructions

Seed memories from text content.

Parses the content into individual memories (one per paragraph or list item) and stores them. Useful for initial setup or bulk import.

Content is split on:

  • Double newlines (paragraphs)

  • Lines starting with '- ' or '* ' (list items)

  • Lines starting with numbers like '1. ' (numbered lists)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesText content to parse and seed memories from
memory_typeNoMemory type for all extracted itemsproject
promote_to_hotNoPromote all to hot cache

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorsYes
memories_createdYes
memories_skippedYes
Behavior3/5

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

The description details how content is split (paragraphs, list items), but lacks information on idempotency, error handling, or whether it overwrites existing memories. No annotations are provided to supplement.

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

Conciseness4/5

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

The description is concise, front-loaded with purpose, and uses a bullet list for splitting rules. It could be slightly more compact, but is clear and well-structured.

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?

Given no output schema details are needed (but it exists), the description covers splitting rules well. However, it omits edge cases like empty content or memory limits, which are important for a bulk import tool.

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?

Schema coverage is 100%, so baseline is 3. The description adds value for 'content' by explaining parsing, but does not expand on 'memory_type' or 'promote_to_hot' beyond the schema.

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

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's verb ('seed') and resource ('memories from text content'), and explains it parses content into individual memories. It distinguishes from 'seed_from_file' by focusing on text input.

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 mentions 'useful for initial setup or bulk import,' providing some context. However, it does not explicitly state when not to use it or compare with alternatives like 'remember' or 'seed_from_file'.

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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