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by Coinversaa
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JavaScript
Remote

Cohort Daily Performance

pulse_cohort_performance_daily
Read-onlyIdempotent

Track daily PnL, volume, trade counts, and active traders by cohort tier to compare group consistency and profitability over time (up to 30 days).

Instructions

Get historical daily performance statistics for trader cohorts. Returns PnL, volume, trade counts, and active trader counts per tier — 32 tier rows per day, so filter with tierType and/or tier for a readable series. Use this to track the consistency and profitability of different groups over time. API default window 30d, max 30d. Returns { rows, totalCount, truncated, note? } sorted by date (newest first) then tierType and tier, capped at limit (default 100); when truncated, narrow with tierType/tier or a shorter since, or raise limit (max 2000). Cohort history is served for up to the last 30 days; see data_coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNoKeep only one tier's rows (either vocabulary). Combine with tierType for a single series.
limitNoMax rows to return, newest date first (default 100, max 2000).
sinceNoTime window for history (max 30d). e.g. '7d', '14d', '30d'
endTimeNoExplicit end time (ISO string or timestamp). Defaults to now.
tierTypeNoKeep only PnL-tier or size-tier rows (applied by the connector; halves the row count).
startTimeNoExplicit start time (ISO string or timestamp). Overrides 'since'.
useToonFormatNoReturn data in compact toon format (default: true). Set to false for standard JSON.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.12.0
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 100,
+  "description": "Max rows to return, newest date first (default 100, max 2000).",
+  "maximum": 2000,
+  "minimum": 1,
+  "type": "integer"
+}
    • addedInput schema / properties / tier
      Added value: +{
+  "description": "Keep only one tier's rows (either vocabulary). Combine with tierType for a single series.",
+  "enum": [
+    "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"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / tierType
      Added value: +{
+  "description": "Keep only PnL-tier or size-tier rows (applied by the connector; halves the row count).",
+  "enum": [
+    "pnl",
+    "size"
+  ],
+  "type": "string"
+}
  2. Addedv0.6.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare a safe read-only, idempotent operation, and the description adds substantial behavior beyond them: a 30-day default/max window, 32 tier rows per day, sort order (date desc, then tierType/tier), the limit cap (100 default / 2000 max), and explicit truncation handling. This is exactly the operational context an agent needs to size its queries correctly.

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 purpose, then return shape, then operational guidance in a logical order. A few clauses restate schema facts (default 100, max 2000, truncation), which is mild redundancy, but the density is justified given the truncation behavior and coverage limits it must convey.

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?

With no output schema, the description carries the return contract itself ({ rows, totalCount, truncated, note? }, sort order) and even routes the agent to the data_coverage sibling for retention limits. Nothing material to calling or interpreting this tool is missing.

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 nevertheless adds value beyond the schema: it explains that 32 rows are produced per day and that combining tier with tierType yields a single series, that tierType 'halves the row count', that startTime overrides since, and that since is capped at 30d. That exceeds schema-level documentation.

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?

States a specific verb+resource ('historical daily performance statistics for trader cohorts') and enumerates the returned measures (PnL, volume, trade counts, active trader counts per tier). This is clearly distinguishable from siblings like pulse_cohort_summary, pulse_cohort_history, and pulse_cohort_positions without opening their schemas.

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?

Gives explicit intent ('track the consistency and profitability of different groups over time') and practical filtering advice ('filter with tierType and/or tier for a readable series'), plus a remedy path when truncated. It does not, however, name or exclude the closest sibling alternatives (e.g. pulse_cohort_history or pulse_cohort_summary), so routing between them is left to inference.

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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