JYOTINT Sealed Forecasts

get_luck_test

The corpus-level 'could this record be luck?' significance test, computed AGAINST the record: 92 graded calls clustered into 68 independent events (correlated calls share one event), strict scoring (one NEAR fails the whole event), luck-prior floored at a coin flip per event. Returns the exact binomial tail, the BREAK-EVEN floor (what a skeptic must grant per event to call it luck), the sensitivity band, the published clusters + failed events, the sittings exhibit (every 2+-call seal date — complete enumeration, 23/23), the miss anatomy (all 4 misses electoral, never a miss at >=0.90), and the PRE-STATED falsification conditions. Caveats ship in the same object — quote them with the numbers. Measures improbability-of-luck, never calibration skill (the aggregate Brier's base-rate tie stays disclosed).

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A3.8/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavior: computation method (92 calls, 68 events), scoring strictness, luck-prior floor, exact return fields, caveats. It is exceptionally detailed about what the tool does and returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and verbose, using many technical terms. It could be more concise by grouping related details or using bullet points. However, it starts with a clear main purpose sentence.

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?

For a tool with no parameters and no output schema, the description covers all necessary context: what is computed, what is returned, and even notes caveats. It leaves no ambiguity about the tool's functionality.

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?

Input schema has zero parameters, so baseline is 4. The description adds meaningful context about the computation but does not need to explain parameters as there are none.

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 identifies the resource (corpus-level record) and action (significance test for luck). It uses specific language like 'binomial tail' and 'break-even floor' but does not explicitly differentiate from sibling tools like get_advisory or get_calibration.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., ask_the_record, get_calibration_and_integrity). The description explains what it does but leaves the agent to infer the appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All JYOTINT Sealed Forecasts tools Inspect

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TDQS

A3.7/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (fetch one forecast vs. list open calls vs. search), but neural_search and search_sealed_forecasts both retrieve from the same corpus, and ask_the_record overlaps slightly with neural_search for site-copy questions. Descriptions mitigate ambiguity, but a few boundaries require careful reading.

Naming Consistency4/5

The dominant pattern is get_<noun> (get_advisory, get_map, get_luck_test), with list_open_calls, search_sealed_forecasts, ask_the_record, and neural_search as deviations. All names are lowercase snake_case and readable, but the verb prefixes are not perfectly uniform.

Tool Count5/5

With 13 tools, the server is well-scoped for a specialized sealed-forecast corpus. Each tool addresses a distinct analytical or retrieval need, and the count fits comfortably in the ideal range without feeling bloated or thin.

Completeness5/5

The tool surface covers the full lifecycle of interacting with the corpus: search, retrieve, list, inspect stats, verify integrity, regrade, visualize, and ask questions. The append-only nature means no update/delete tools are needed, so the set is complete for its stated purpose.

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