JYOTINT Sealed Forecasts

get_calibration_and_integrity

Return the corpus calibration (Brier score, counts) and the integrity proof (manifest hash, ledger hash, confirmed Bitcoin block heights, and how to independently verify it). ALSO returns record_versions: the record is append-only, so if a publication cited a count/Brier that no longer matches the live count, that is expected (calls were sealed since) — resolve the paper's exact cited state by record count or hash via record_versions and recompute the immutable frozen snapshot.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: the record is append-only, values change over time, and how to independently verify using hashes and Bitcoin block heights. This level of transparency is exceptional and compensates for the lack of annotations.

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 two sentences, front-loading the primary return values and then explaining the append-only behavior and record_versions utility. It is efficient but the second sentence is dense and could be slightly tighter. Still, it earns its place.

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?

Given no output schema, the description lists all return components (calibration, integrity proof, record_versions) and explains how to interpret record_versions for resolving state discrepancies. This is comprehensive for the tool's purpose and complexity.

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?

The input schema has zero parameters, and schema coverage is 100% (vacuously). Per the guidelines, a baseline of 4 is appropriate. The description adds no param info, which is unnecessary since there are none.

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 clearly specifies that the tool returns corpus calibration, integrity proof, and record_versions, detailing the exact components (Brier score, manifest hash, ledger hash, etc.). This distinguishes it from sibling tools like 'get_corpus_insights' or 'get_advisory' by focusing on calibration and verifiable proofs.

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 explains that record_versions resolves discrepancies when cited states no longer match live data, guiding the user on when to use the tool to get current state and verify past states. It does not explicitly list when not to use or alternatives, but the context is clear enough for an AI agent.

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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