Regime

Calendar check

calendar_check

Read-onlyIdempotent

'Uptober'. 'Mondays dip'. 'Sell in May'. Any calendar claim on 34 coins, measured against chance. With seven days one has to come first, so the answer is a permutation test: the SAME returns dealt out at random hundreds of times, and how often chance alone produces a bucket that good. Plus how many times the bucket really happened - October is 279 days of bitcoin but nine Octobers - and the round-trip cost. Nine years of daily candles.

Tests whether one day of the week, month of the year or hour of the day really beats the rest for a coin. Use for seasonality claims (Uptober, Monday dips, Sell in May). For the claim about missing the market's best days use best_days_check; for when to spread an entry, averaging_in_check.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`coin`	Yes	Ticker or full pair. An unknown one comes back with the list we hold and the ones left out.
`calendar`	Yes	day_of_week, month_of_year or hour_of_day. The hourly one exists for the 16 coins we hold hourly candles for.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No	false when we do not hold that data. Never a zero standing in for an answer.
`page`	No	The page with these exact numbers already in.
`error`	No	no_data when ok is false.
`model`	No	The test, stated, including why a monthly p-value flatters itself.
`reason`	No	Why, in one sentence, and what we do have instead.
`source`	No	The public file these numbers come from.
`buckets`	No	Every bucket: its name, observations, times occurred, mean, median and share of up days.
`history`	No	From, to, days held and which tape.
`shuffles`	No	How many shuffled worlds were tested.
`best_bucket`	No	The best bucket by average move, named.
`observations`	No	Moves measured across all buckets.
`worst_bucket`	No	The other end.
`best_mean_pct`	No	Its average move. On its own, this is the advertisement.
`worst_mean_pct`	No	Its average move.
`edge_covers_cost`	No	Whether the average move of the best bucket is bigger than that cost. Usually it is not.
`chance_best_p95_pct`	No	What chance reaches in one shuffle out of twenty.
`round_trip_cost_pct`	No	What entering and exiting once costs, so the edge can be compared with it.
`beats_chance_at_5pct`	No	Whether that share is under 0.05. Across all 34 coins, about 5% of these come back true by chance alone, so one true answer on its own is not a finding.
`chance_best_mean_pct`	No	What the BEST bucket of a shuffled world averages. Anything below this is less impressive than nothing.
`chance_matches_it_share`	No	The p-value: share of shuffles whose best bucket matched or beat the real one. High means no pattern.
`best_bucket_happened_times`	No	How many times that bucket has actually occurred. For months this is years, not days: nine Octobers is nine observations however many candles they hold, and it is the number these claims never show.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 2, 2026
- addedInput schema / properties / calendar / enum
```
Added value: +[
+  "day_of_week",
+  "month_of_year",
+  "hour_of_day"
+]
```
First observedOct 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent and non-destructive, so safety is covered. The description adds genuine methodological context: a permutation test with hundreds of randomized shuffles, the reported round-trip cost, and sample-size caveats such as 'nine Octobers'. This is real disclosure beyond the structured fields, though it does not describe output shape (which the output schema handles).

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 operative routing sentence and alternatives are front-loaded and tight. The opening flavor paragraph ('Uptober', 'Mondays dip', sample-size asides) is stylized and longer than strictly necessary, but it does establish the seasonality framing before the definitions.

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?

For a read-only analytical tool with annotations covering safety and an output schema covering returns, the description supplies the method, data scope (nine years of daily candles, 34 coins) and sibling routing. Complete enough to invoke correctly; only the verbose framing keeps it from being maximally efficient.

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% and both parameters are already documented, including the enum values and the hourly limitation. The prose restates the three calendar buckets but adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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 analytical action ('Tests whether one day of the week, month of the year or hour of the day really beats the rest for a coin') with a clear resource, and names the sibling tools it is not. An agent can distinguish it from best_days_check and averaging_in_check without opening a schema.

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?

Explicitly says to use it for seasonality claims and gives concrete examples (Uptober, Monday dips, Sell in May). It then routes the agent away with named alternatives: best_days_check for the best-days claim, averaging_in_check for entry spreading.

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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