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get_instinct

Retrieve the complete record for a known pattern key to see full context, confidence, and metadata. Use after a compact suggestion to get detailed information.

Instructions

Fetch the full record for one pattern by its exact key.

    Exact-match lookup; keys are case-sensitive and must include the
    category prefix. Typically called after suggest() returns a compact
    entry and you want the full context. Read-only.

    For substring or keyword search use search_instincts(). For browsing
    by category/project without a key use list_instincts(). For the
    confidence timeline of this pattern, pair with history(pattern).

    Args:
        pattern: Exact pattern key including its category prefix.
            Examples: "seq:lint->fix->lint", "pref:style=black",
            "fix:null-deref-guard", "combo:eslint+prettier". Case-sensitive.

    Returns:
        On hit — the full record: {"pattern": str, "category":
        "sequence"|"preference"|"fix_pattern"|"combo", "confidence": int,
        "promoted": 0|1|2|3, "level": "raw"|"mature"|"rule"|"universal",
        "project": str ("" = global), "source": str, "metadata": dict,
        "explain": str, "first_seen": iso8601, "last_seen": iso8601}.

        On miss — {"error": "Not found: <pattern>"}. Callers must check
        for the "error" key before assuming a record was returned.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patternYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavior: exact-match, case-sensitive, read-only, requires category prefix, returns full record on hit or error on miss, and instructs callers to check for error key. Provides rich behavioral context.

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, bullet points, and examples. While slightly verbose, every sentence adds value; could be slightly more concise but remains clear.

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 single parameter and existence of output schema, the description is thorough: covers input format, return structure, error handling, and usage context. Leaves no gaps for a simple lookup tool.

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 coverage is 0%, but the description provides extensive detail: examples of valid patterns, case-sensitivity, requirement for category prefix. This adds critical meaning beyond the bare 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 'Fetch the full record for one pattern by its exact key,' which is a specific verb+resource combination. It distinguishes from sibling tools like search_instincts and list_instincts by contrasting exact-match versus substring search or browsing.

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 (after suggest() returns a compact entry) and when to use alternatives (search_instincts for substring, list_instincts for browsing, history for confidence timeline). Provides clear usage context.

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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