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add_memory

Store user preferences, personal context, project patterns, and decisions to long-term memory for future reference.

Instructions

Save important information to long-term memory.

When to Use

  • User stated preferences ('User prefers dark mode')

  • Personal context ('User is learning Rust', 'User works on payments team')

  • Project patterns ('API endpoint at /api/v2', 'Uses PostgreSQL for this project')

  • Agreed decisions ('Team decided to use Docker for deployment')

  • Constraints ('Budget is tight', 'Deadline is end of month')

  • Lessons learned ('Don't use library X, it caused issues')

Post-Response Storage Pattern (IMPORTANT)

After responding to user, EVALUATE whether new facts should be stored:

  1. Did user share personal context? → Store it

  2. Did we make a technical decision? → Store it

  3. Did user express a preference? → Store it

  4. Did user correct previous information? → Update existing memory

Deduplication

Search before adding if the info seems routine. Prefer UPDATING existing memories when finding conflicting info. Store each distinct piece as a separate memory with clear, searchable text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesMemory content - write clear, searchable statement (e.g., 'User prefers dark mode theme').
user_idNoUser identifier (optional, uses default if not set).
agent_idNoSession/run identifier for grouping related memories.
metadataNoAdditional metadata (runId supported).
Behavior4/5

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

No annotations provided, but the description discloses deduplication behavior ('Search before adding', 'Prefer UPDATING existing memories') and storage pattern. Does not mention return value or side effects, but acceptable for a simple store tool.

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?

Well-structured with headings and bullet points, front-loaded with purpose. Slightly long but every sentence adds value. Could be shorter, but not verbose.

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?

Covers usage scenarios, deduplication, storage pattern, and parameter guidance. No missing information given the tool's simplicity and lack of output schema.

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% with all parameters described. The description adds value for the 'text' parameter by advising to write 'clear, searchable statements'. No additional detail for other params beyond 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 'Save important information to long-term memory' and provides specific examples of what to store (preferences, context, decisions). It distinguishes from siblings like search_memories, update_memory, and delete_memory.

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 'When to Use' section explicitly lists categories of information (preferences, personal context, project patterns). The 'Post-Response Storage Pattern' gives procedural guidance. Missing explicit when-not-to-use, but adequate.

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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