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cachly-dev

Cachly — AI Cognitive Brain

auto_learn_session

Automatically capture and classify lessons from session observations without explicit learning calls. Use at session end to store commands, errors, and solutions.

Instructions

Auto-learn from a list of session observations WITHOUT explicit learn_from_attempts calls. Pass what happened (commands run, errors seen, solutions found) and the brain classifies and stores lessons automatically. Use at session_end to capture everything you did, even if you forgot to call learn_from_attempts. Returns a summary of what was auto-stored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesUUID of the cache instance
observationsYesList of observations from this session
Behavior3/5

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

Discloses core automation (classifies/stores lessons) and return value. Lacks details on idempotency, potential side effects, or limits. Adequate given no 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?

Two focused sentences with no wasted words. Front-loaded with essential purpose and usage context.

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?

Explains input (observations) and output (summary) sufficiently. With 100% schema coverage and no output schema, the description is nearly complete. Could mention structured observation items but schema handles that.

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 covers 100% of parameters; description adds some context like 'what happened' but does not significantly extend meaning beyond the schema field descriptions.

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?

Clearly states the tool's function: auto-learn from observations without explicit calls, differentiating from learn_from_attempts. It identifies the input (observations) and output (summary).

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 suggests use at session_end as a safety net for missed learn_from_attempts. Provides context but does not list all alternative tools or scenarios to avoid.

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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