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list_instincts

Audit and debug recorded patterns with filters for confidence, category, project. Includes low-confidence observations for noise analysis and promotion debugging.

Instructions

List recorded patterns with optional filters; includes low-confidence observations.

    Unlike suggest() — which returns mature-only, compact payloads — this
    surfaces seedlings too. Useful for audits, pruning noise via gc() or
    alias_pattern(), and debugging why a pattern has not promoted.
    Read-only; records sorted by confidence descending, then last_seen
    descending.

    For day-to-day agent guidance prefer suggest(). For keyword search
    use search_instincts(). For a single exact-key lookup use
    get_instinct().

    Args:
        min_confidence: Minimum observation count (inclusive). Examples:
            1 returns everything including one-offs; 5 returns mature+;
            10 returns rules only.
        category: Filter by pattern type. One of: "sequence",
            "preference", "fix_pattern", "combo". Empty string = all.
        project: Filter by project fingerprint (repo hash or path hash).
            Empty string returns every project including the global ""
            bucket.
        limit: Maximum records to return. Default 50; raise for full
            dumps, lower for top-N views.

    Returns:
        {"instincts": [<record>, ...], "count": int, "hint": str}

        Each <record> has: "pattern" (str key with prefix),
        "category" ("sequence"|"preference"|"fix_pattern"|"combo"),
        "confidence" (int observation count),
        "promoted" (0=raw, 1=mature, 2=rule, 3=universal),
        "level" ("raw"|"mature"|"rule"|"universal" — string form of promoted),
        "project" (str fingerprint, "" = global),
        "source" (str origin tag), "metadata" (parsed dict),
        "explain" (str human-readable note),
        "first_seen" and "last_seen" (ISO 8601 timestamps).

        "hint" points to suggest() / search_instincts() / get_instinct()
        as next-step tools depending on the audit goal.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_confidenceNo
categoryNo
projectNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden. It declares read-only behavior and sorting order, and notes that low-confidence observations are included. However, it does not mention rate limits or any other behavioral constraints.

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

Conciseness4/5

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

The description is well-structured with sections and front-loaded purpose. While informative, it is slightly verbose with detailed parameter explanations; a more concise version could still be effective.

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 4 parameters, no required params, and an output schema, the description covers purpose, usage, parameters, return structure, and next-step hints. It is fully sufficient for an AI agent to select and use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description thoroughly explains each parameter: min_confidence with examples, category with allowed values, project with fingerprint explanation, and limit with default and usage. This adds significant meaning beyond the schema.

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 lists recorded patterns with filters including low-confidence observations, and distinguishes itself from siblings like suggest(), search_instincts(), and get_instinct() by contrasting scope and use cases.

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?

The description provides explicit guidance on when to use this tool vs alternatives: 'For day-to-day agent guidance prefer suggest(). For keyword search use search_instincts(). For a single exact-key lookup use get_instinct().' It also lists specific use cases like audits and debugging.

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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