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WRG-11

instinct

by WRG-11
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

trending

Identify patterns with the highest observation velocity in a configurable time window, surfacing currently active trends for prioritization.

Instructions

Rank patterns by observation velocity (reinforcements per window).

    Counts entries in the confidence log per pattern within the window
    and returns the busiest. A brand-new pattern observed 10 times
    today outranks a long-mature pattern idle for weeks. Falls back to
    last_seen ordering for patterns with no log entries (pre-history
    data). Read-only.

    For all-time leaderboards use list_instincts(min_confidence=10).
    For the confirmation-rate view of whether trending patterns were
    actually useful, pair with effectiveness(days).

    Args:
        days: Window size in days. Default 7. Smaller (1) = what is
            hot right now; larger (30) = what is steady over the
            month.
        limit: Max patterns to return. Default 10, ordered by window
            observation count descending.

    Returns:
        {"trending": [<record>, ...], "period_days": int}

        Each <record> is a full pattern record (see list_instincts
        for field list) augmented with an extra field
        "observations_in_period": int — the number of reinforcements
        counted in this window. When the fallback path runs (no log
        history), this field is absent; ordering is then by
        "confidence" and "last_seen" descending.

        "period_days" echoes the input so callers can cache results
        against a window size.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

No annotations provided, but description fully discloses read-only behavior, fallback path, and return structure including extra field and its absence. Comprehensive 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?

Front-loaded with key point. Every sentence adds value, but slightly lengthy. Could be trimmed slightly without losing clarity.

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?

Output schema exists, but description complements it with detailed return format explanation, including extra field and fallback behavior. Complete and comprehensive.

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 description adds extensive semantics: days explains window size for hot vs steady, limit explains max patterns and ordering. Adds significant value beyond 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 ranks patterns by observation velocity (reinforcements per window). It distinguishes itself from sibling tools like list_instincts (all-time leaderboards) and effectiveness (confirmation rate).

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?

Explicit guidance: use for ranking by velocity, fallback ordering. Alternatives named: list_instincts for all-time, effectiveness for confirmation rate. Parameter guidance: days window size for hot vs steady patterns.

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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