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extract_principles

Analyze memory clusters to extract factual principles and patterns from your project. Use dry_run to preview candidates before generating.

Instructions

Analyze memory clusters and extract underlying factual principles or patterns. Principles are observations about your project — "this codebase tends to have X" — not behavioral instructions (those are skills). Use dry_run to preview candidates first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subjectNoFocus on a specific subject area. Omit to scan all subjects.
project_idNoProject to extract principles for (auto-detected if omitted)
dry_runNoWhen true, preview principle candidates without generating them
Behavior2/5

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

With no annotations, the description carries the full burden. It states the tool extracts patterns but does not disclose whether it modifies memory, requires permissions, or what the output format is. The behavioral transparency is insufficient.

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 concise: two sentences that clearly define the tool and provide a key usage recommendation. No unnecessary words or repetition.

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?

The description covers the core purpose and a usage tip but lacks details on output format, error handling, or prerequisites. For a tool with three parameters and no output schema, it is marginally adequate but not fully complete.

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?

All three parameters have 100% schema description coverage, so the description adds little beyond what the schema already provides. It offers context for the dry_run parameter but does not significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes memory clusters to extract principles, distinguishing them from behavioral instructions. However, the exact nature of 'principles' could be more concretely defined, but overall it's specific.

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

Usage Guidelines3/5

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

The description recommends using dry_run first, providing a clear usage hint. However, it does not differentiate this tool from sibling tools like generate_skills or specify when not to use it, lacking explicit alternatives.

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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