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grahammccain

Chart Library

by grahammccain
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Python
Remote

analyze

Read-onlyIdempotent

Analyze historical patterns by computing metrics such as anomaly, volume profile, crowding, correlation shifts, and earnings reactions on a cohort or symbol-date anchor.

Instructions

Analytic metrics on a cohort or (symbol, date) anchor.

metric values:
  - "anomaly"             — is the pattern unusual vs the symbol's
                              own history? (needs symbol)
  - "volume_profile"      — intraday volume vs historical norms
                              (needs symbol)
  - "crowding"            — cross-symbol crowding indicator
                              (market-wide; no symbol needed)
  - "correlation_shift"   — rolling correlation breakdowns
                              (extra_args: lookback, window, symbols)
  - "earnings_reaction"   — historical earnings gap reactions
                              (needs symbol; extra_args: min_gap)
  - "pattern_degradation" — are signals losing edge vs historical
                              accuracy? (market-wide)
  - "regime_accuracy"     — win rates filtered by current regime
                              (needs symbol)
  - "decompose"           — find slice conditions that separated
                              winners from losers within a cohort
                              (needs cohort_id; horizon, max_slices,
                              explain_slices apply)
  - "clusters"            — cluster a cohort into k forward-return
                              groups (needs cohort_id; horizon, k)

Supply cohort_id (preferred, anchor inherited) OR explicit
symbol+date for the symbol-needing metrics.

Args:
    metric: see list above
    cohort_id: handle from `search` or `cohort` (required for
        decompose, clusters; preferred for symbol-needing metrics)
    symbol, date: explicit anchor when no cohort_id available
    extra_args: per-metric optional kwargs (see metric list)
    horizon: forward horizon in trading days (decompose, clusters,
        regime_accuracy)
    max_slices: max returned slice conditions (decompose)
    explain_slices: include Haiku narrative tying slices together
        (decompose)
    k: cluster count override (clusters; default chosen automatically)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricYes
cohort_idNo
symbolNo
dateNo
extra_argsNo
horizonNo
max_slicesNo
explain_slicesNo
kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds that it computes analytics (consistent with read-only) but does not disclose additional behavioral traits beyond what annotations provide.

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 bullet-pointed metric list and Args section, but it is somewhat lengthy. It front-loads the core purpose and each sentence adds value, though some redundancy exists (e.g., repeating 'needs symbol').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description adequately covers input parameters and their dependencies. It lacks mention of error handling or data constraints, but overall is quite comprehensive for a complex tool with 9 parameters.

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?

Despite 0% schema description coverage, the description provides detailed semantics for all 9 parameters, including the list of metrics with requirements, explanation of cohort_id vs symbol/date, and per-parameter defaults and usage (e.g., extra_args, horizon, max_slices).

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 it provides analytic metrics on a cohort or symbol/date anchor, listing specific metrics. However, some metrics (clusters, decompose) have sibling tools with the same names, creating potential confusion.

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 explains how to supply cohort_id vs symbol+date and which metrics need symbol, but it does not explicitly address when to use this tool over sibling tools like cohort_analyze, clusters, or decompose.

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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