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sage_reflect

Capture task outcomes by recording successful approaches (dos) and mistakes (don'ts) to build institutional memory that improves agent performance over time.

Instructions

End-of-task reflection. Call this after completing a significant task to store what went right (dos) and what went wrong (don'ts). This feedback loop is critical — Paper 4 proved that agents with memory achieve Spearman rho=0.716 improvement over time while memoryless agents show rho=0.040 (no learning). Both successes and failures make you better. Store them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoKnowledge domain (e.g. debugging, architecture, user-prefs)general
dontsNoWhat went wrong — mistakes made, approaches that failed, things to avoid
dosNoWhat went right — approaches that worked, patterns to repeat
task_summaryYesBrief description of what the task was
Behavior4/5

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

No annotations provided, so the description carries the burden. It explains the tool stores feedback and emphasizes its importance for learning, implying an additive, non-destructive operation.

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

Conciseness3/5

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

The description is somewhat verbose with a research paper reference, but it is front-loaded with the core purpose. Could be more concise without losing meaning.

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

Completeness4/5

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

Given the tool is simple, has no output schema, and annotations are absent, the description provides adequate context: when to use, what to store, and why. It doesn't cover return values but that's acceptable.

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 description coverage is 100%, so baseline is 3. The description mentions 'dos' and 'don'ts' but adds no additional meaning beyond what the schema already provides for parameters.

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 'End-of-task reflection' and specifies the action: store dos and don'ts. It distinguishes from sibling tools like sage_remember (general fact storage) by focusing on task reflection.

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?

Explicitly says 'Call this after completing a significant task,' providing clear usage context. It does not mention when not to use it or alternatives, but the family of sibings implies differentiation.

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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