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JavaScript
Remote

Live Cohort Bias History

live_cohort_bias_history
Read-onlyIdempotent

Get historical hourly net-bias snapshots per cohort to answer whether smart-money was accumulating or exiting and which tier flipped first.

Instructions

Get historical cohort bias data for a specific coin. Use this when a user asks 'were smart-money cohorts accumulating or exiting?' or 'which tier flipped first?'. Returns hourly net-bias snapshots for each tier or for a specific tier over time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesCoin symbol (e.g. BTC, ETH, SOL)
tierNoSpecific tier to track. Omit for all tiers in the category.
hoursNoNumber of hours of history (default 168 = 7 days, max 720 = 30 days)
tierTypeNoTier category: 'pnl' for profit tiers, 'size' for volume tierspnl
useToonFormatNoReturn data in compact toon format (default: true). Set to false for standard JSON.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.0
    • changedInput schema / properties / tier / enum
      Previous value: -[
-  "money_printer",
-  "smart_money",
-  "grinder",
-  "humble_earner",
-  "exit_liquidity",
-  "semi_rekt",
-  "full_rekt",
-  "giga_rekt",
-  "leviathan",
-  "tidal_whale",
-  "whale",
-  "small_whale",
-  "apex_predator",
-  "dolphin",
-  "fish",
-  "shrimp"
-]New value: +[
+  "apex",
+  "sharps",
+  "grinders",
+  "scrapers",
+  "crowd",
+  "bleeders",
+  "trapped",
+  "blown_out",
+  "heavyweights",
+  "cruiserweights",
+  "middleweights",
+  "welterweights",
+  "lightweights",
+  "featherweights",
+  "flyweights",
+  "strawweights",
+  "money_printer",
+  "smart_money",
+  "grinder",
+  "humble_earner",
+  "exit_liquidity",
+  "semi_rekt",
+  "full_rekt",
+  "giga_rekt",
+  "leviathan",
+  "tidal_whale",
+  "whale",
+  "small_whale",
+  "apex_predator",
+  "dolphin",
+  "fish",
+  "shrimp"
+]
  2. Addedv0.6.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, non-destructive), so the description only needs to add context. It does so by disclosing that the tool returns hourly net-bias snapshots and can return all tiers or a single tier over time. It adds useful behavioral detail without contradicting the annotations.

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?

Three sentences, each earning its place: purpose, usage triggers, and return shape. The most important information is front-loaded, and there is no filler or repetition of schema details.

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?

With no output schema, the description adequately characterizes the return value as hourly net-bias snapshots per tier over time. It covers the main parameter semantics and usage context. It does not describe edge cases like unknown coins or value units, but the schema covers parameters and annotations cover safety, making this sufficient for most invocation scenarios.

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?

Schema coverage is 100%, so the baseline is 3. The description adds semantic value beyond the schema by explaining what 'bias' means in context ('accumulating or exiting') and by clarifying how the tier parameter behaves ('each tier or a specific tier'). This goes beyond the schema's terse field descriptions.

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 states a specific verb ('Get'), a clear resource ('historical cohort bias data for a specific coin'), and a precise scope. It also includes example user questions that make the purpose immediately understandable and distinguish it from the sibling 'live_cohort_bias' by emphasizing 'historical' and 'hourly... over time'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit trigger phrases ('were smart-money cohorts accumulating or exiting?' and 'which tier flipped first?'), which is strong when-to-use guidance. It does not mention when not to use this tool or point to alternatives like pulse_cohort_bias_history, so it falls short of a 5.

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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