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Remember a fact

save_fact

Store user preferences, decisions, and personal details permanently across sessions. Automatically capture corrections, troubleshooting fixes, and new contacts for future AI interactions.

Instructions

Saves a new persistent fact about the user that will be available in all future sessions across every AI tool. Use whenever the user shares anything worth keeping -- even if they don't explicitly ask: 'capture', 'noted', 'remember this', 'log', 'store', 'don't forget', or any preference, decision, correction, contact, project detail, technical choice, troubleshooting fix, or personal detail the user mentions. When the user reports a solved client/tool issue, save it as category 'troubleshooting' using the shape 'Issue: . Solution: '. Also trigger proactively when the user corrects you (save the correction immediately), reveals a preference by rejecting something, or names a person/tool/project for the first time. Do NOT trigger for transient values (today's weather, one-off calculations, temporary state that won't matter next session). Do NOT trigger for facts already confirmed stored earlier in this session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
factYesThe fact -- a single atomic statement
categoryNoCategory: preference, decision, technical, contact, project, troubleshooting, generalgeneral
confidenceNo
source_session_idNoSession ID where this fact was learned
preserve_as_blobNoIf true, stores as a blob and extracts atomic facts via LLM instead of saving fact text directly
commit_shaNoGit commit SHA linking this fact to a code change (for audit trail)
pr_numberNoPR number linking this fact to a code review (for audit trail)
Behavior4/5

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

No annotations provided, so description fully bears the burden. It discloses persistence across sessions and proactive saving triggers. It does not describe side effects like overwriting or return values, but the core behavioral traits are well covered.

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 lengthy but every sentence serves a purpose. It front-loads the core purpose and then provides rich usage guidance. Could be slightly tighter, but the depth justifies the length.

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 the tool's importance for long-term memory and 7 parameters, the description covers proactive triggers, exclusions, formatting, and context. No output schema exists, but the purpose is straightforward save.

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

Parameters4/5

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

Schema coverage is 86%, already good. Description adds value by specifying the 'troubleshooting' category format and explaining when to use 'preserve_as_blob'. It does not detail all parameters but complements the schema effectively.

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 it 'Saves a new persistent fact about the user that will be available in all future sessions across every AI tool.' This is a specific verb+resource combination and distinguishes it from all sibling tools (none of which are memory-related).

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

Usage Guidelines5/5

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

Provides explicit when-to-use examples (preferences, decisions, corrections, etc.), when-not-to-use (transient values, already confirmed facts), and alternatives not needed as it's the sole fact-saver. Also gives formatting instructions for troubleshooting.

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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