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store_memory

Save information like preferences, lessons, or project context to a persistent local database. Memories are retained across sessions and can be retrieved later.

Instructions

Store a new memory in the local SQLite database. Creates a persistent row with an auto-generated UUID, timestamp, and the provided content. Use for preferences, lessons learned, project context, relationship notes, or general information worth remembering across sessions. Memories persist across sessions and are surfaced by boot_report, search_memory, and list_memories. Returns the generated ID on success. Does not deduplicate — calling twice with the same content creates two separate memories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe memory content to store. Plain text, no length limit. Should be self-contained — future retrieval may return this memory without surrounding context.
categoryNoMemory category. Determines how the memory is weighted during orientation: 'preference' and 'relationship' contribute to the orientation score. 'context' is used for checkpoints and session handoffs. 'lesson' is used for constraints and warnings. 'general' is the default catch-all.general
importanceNoImportance score from 1 (low) to 10 (critical). Higher-importance memories are ranked first in search results. Constraints default to 9, checkpoints to 8, general notes to 5.
projectNoProject name to scope this memory to. When set, boot_report can filter orientation to this project. Omit for cross-project memories.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses persistence across sessions, no deduplication, return value (generated ID), and that content should be self-contained. It also notes that memories are surfaced by other tools. No destructive behavior is relevant, and the description covers the key behavioral traits.

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 a single paragraph of about 6 sentences, well-structured: action, details, usage examples, cross-references to other tools, return value, and a caveat. Every sentence earns its place without redundancy.

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

Completeness5/5

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

Given 4 parameters with full schema descriptions, no output schema but return value explained, and no nested objects, the description is complete. It covers persistence, deduplication, and cross-tool integration, leaving no significant gaps for an AI agent to use this tool correctly.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant value beyond the schema descriptions. For 'content' it specifies plain text with no length limit; for 'category' it explains impact on orientation scoring; for 'importance' it clarifies ranking and defaults for specific use cases; for 'project' it explains scoping and omission. This enriches the agent's understanding.

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 verb 'Store' and the resource 'memory', and identifies it as persistent with auto-generated UUID and timestamp. It lists specific use cases (preferences, lessons, etc.) and explicitly connects to sibling tools (boot_report, search_memory, list_memories), distinguishing this creation tool from retrieval and deletion tools.

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

Usage Guidelines4/5

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

The description enumerates appropriate use cases (preferences, lessons, project context, etc.) and warns about the lack of deduplication. While it does not explicitly state when not to use, the positive guidance is strong and the deduplication caveat helps avoid misuse.

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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