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Averaging in check

averaging_in_check
Read-onlyIdempotent

Whether spreading the entry would have helped, measured. One amount into a coin over a window (90, 365 or 730 days): all of it on day one, or equal instalments weekly or monthly. Both start the same day, move the same money and are valued on the same day, started on every day of the history. You get how often spreading ended ahead, the middle result of each, and the worst start day of each — which is what spreading actually buys and the half nobody shows. Spot tape, no fees, dead coins included.

Use for dollar-cost averaging versus lump sum on one coin. For claims about particular days or months use best_days_check or calendar_check; for the fall after a single buy, drawdown_check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesTicker or full pair. An unknown one comes back with the list we hold and the ones left out.
daysYesThe window, in days: 90, 365 or 730.
instalmentsYesHow often a slice goes in: weekly or monthly. Defaults to weekly.weekly

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNofalse when we do not hold that data. Never a zero standing in for an answer.
errorNono_data when ok is false.
modelNoThe assumptions, stated: cash not yet in earns nothing (against spreading), no fees (in favour of spreading).
reasonNoWhy, in one sentence, and what we do have instead.
sourceNoThe public file these numbers come from.
startsNoStart days tested. Fewer than 120 and we refuse to give a percentage.
instalmentsNoHow many slices fit in the window.
spreading_wonNoStart days on which spreading it out ended ahead. A count, not a rate: «100%» is only said when it was all of them.
p05_spread_pctNoThe same for spreading.
p05_lump_sum_pctNoFifth percentile of starts, lump sum: one worst day is an anecdote.
worst_spread_pctNoThe same for spreading. This pair is the whole argument for averaging in, and it is the one that never appears next to the advice.
median_spread_pctNoMiddle start, spread out.
spreading_won_pctNoThe same as a share of starts.
median_edge_pointsNoPercentage points spreading made over lump sum in the middle case. Negative means lump sum won.
worst_lump_sum_pctNoThe single worst start day, all in on day one.
median_lump_sum_pctNoMiddle start, all in on day one.
share_in_profit_spread_pctNoThe same for spreading.
share_in_profit_lump_sum_pctNoShare of starts that ended up, lump sum.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / days / enum
      Added value: +[
      +  90,
      +  365,
      +  730
      +]
    • addedInput schema / properties / instalments / default
      Added value: +"weekly"
    • addedInput schema / properties / instalments / enum
      Added value: +[
      +  "weekly",
      +  "monthly"
      +]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already confirm this is a read-only, idempotent, non-destructive check. The description adds substantial behavioral context: spot tape only, no fees, dead coins included, both strategies start the same day and use the same money, and the result includes how often spreading won, median outcomes, and worst start days.

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 front-loaded with the core comparison and then moves into methodology and routing guidance. It is longer than strictly necessary and includes colloquial phrases like 'the half nobody shows' and 'Spot tape,' but most sentences carry operational meaning.

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 full schema coverage, annotations, and an output schema, the remaining burden is usage and behavioral context. The description supplies both, including methodology assumptions and what the result conveys, so an agent has enough to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the coin, days enum, and instalments enum/default. The description restates the same information in prose but does not add constraints or syntax beyond the schema, making the baseline 3 appropriate.

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 comparison: whether spreading an entry would have helped, using one amount into one coin over a fixed window, either lump sum on day one or equal weekly/monthly instalments. It also distinguishes itself from siblings like best_days_check, calendar_check, and drawdown_check.

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?

It explicitly says to use this for dollar-cost averaging versus lump sum on one coin, then names the alternatives for adjacent questions: best_days_check or calendar_check for particular days/months, and drawdown_check for the fall after a single buy.

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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