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add_fact

Adds atomic facts like preferences, decisions, or important information to memory for AI agent workflows. Stores single statements such as 'Team prefers Tailwind over CSS modules' with categorization.

Instructions

Add a new atomic fact to UnClick Memory. One fact = one statement. Use when the user states a preference, makes a decision, or shares important info. Good: 'Team prefers Tailwind over CSS modules'. Bad: 'We talked about styling'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
factYesThe fact - a single atomic statement
categoryNoCategory: preference, decision, technical, contact, project, generalgeneral
confidenceNo
source_session_idNoSession ID where this fact was learned
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a write operation ('Add') and specifies the atomic nature of facts, but does not cover other behavioral aspects such as permissions, idempotency, error handling, or rate limits. The description adds some context but leaves gaps typical for a mutation tool without annotations.

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 front-loaded with the core purpose, followed by usage guidelines and examples. Every sentence earns its place by providing essential information without redundancy. It is appropriately sized and structured for quick comprehension.

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 the tool's complexity (a write operation with 4 parameters, no output schema, and no annotations), the description is adequate but incomplete. It covers purpose and usage well but lacks details on behavioral traits, error handling, and return values. For a mutation tool without annotations or output schema, more contextual information would be beneficial.

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?

The description does not explicitly mention parameters, but it adds semantic value by defining what constitutes a valid 'fact' (e.g., atomic statement, examples like 'Team prefers Tailwind over CSS modules'). With 75% schema description coverage, the schema documents parameters well, so the description compensates by clarifying the core 'fact' parameter's meaning beyond the schema's technical description.

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 purpose with specific verb+resource: 'Add a new atomic fact to UnClick Memory.' It distinguishes from siblings by specifying 'One fact = one statement' and provides concrete examples of what constitutes a valid fact versus an invalid one, making the purpose highly specific and actionable.

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 provides clear context on when to use this tool: 'Use when the user states a preference, makes a decision, or shares important info.' It includes good and bad examples to guide proper usage. However, it does not explicitly mention when not to use it or name alternatives among sibling tools, which prevents a perfect score.

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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