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Analyze learning patterns and project health by summarizing counts, maturity levels, category distributions, and project activity for AI coding agents.

Instructions

Summarize the instinct store: counts, levels, categories, projects.

    Use this for a quick health check — how many patterns exist, how
    many have matured, which categories dominate, which projects have
    the most learning. Read-only; no side effects.

    Returns:
        Dict with keys: "total" (int), "by_level" ({seedling, mature,
        rule}), "by_category" ({sequence, preference, fix_pattern,
        combo}), "by_project" (dict of fingerprint -> count), "oldest"
        and "newest" ISO timestamps.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries full burden. It clearly discloses key behavioral traits: 'Read-only; no side effects' establishes safety profile, and the detailed return structure provides transparency about output format. It doesn't mention performance characteristics like execution time or data freshness, but covers the essential operational behavior.

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?

Perfectly structured with three distinct sections: purpose statement, usage guidance, and return specification. Every sentence earns its place - the first establishes what it does, the second when to use it, and the third what to expect. No wasted words or redundant information.

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 has no parameters, has an output schema (implied by the detailed return description), and no annotations, the description provides complete context. It covers purpose, usage, behavioral characteristics, and detailed return format - everything needed for an agent to understand and invoke this tool correctly.

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?

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately notes this is a parameterless operation that provides a comprehensive summary, which aligns perfectly with the empty input schema. No additional parameter information is needed or provided.

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 specific verb 'summarize' and resource 'instinct store' with detailed scope ('counts, levels, categories, projects'). It distinguishes from siblings like 'list_instincts' (listing) or 'search_instincts' (filtering) by focusing on aggregated statistics rather than individual records.

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?

Explicitly states when to use: 'for a quick health check' with concrete examples ('how many patterns exist, how many have matured, which categories dominate, which projects have the most learning'). It also distinguishes from alternatives by emphasizing this is for summary statistics rather than detailed analysis or filtering operations.

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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