JYOTINT Sealed Forecasts

Bitcoin-anchored sealed-forecast record: search, grades, calibration, luck test. Read-only, no key.

使うべきか

品質と安全性

A
説明の品質
100%
スキーマの完全性
65%
命名の品質
97%
ポイズニングのリスク
100%
権限の一致
100%
プロトコルへの準拠
100%

ツール定義とプロトコルへの準拠に関する自動分析に基づいています。

コンテキストコスト

~2,576トークン数(ツール定義)
~342 B一般的なレスポンスサイズ
注意への影響は大きい(128k コンテキストの 2.01%)

これは、サーバーのツールがモデルのコンテキストに読み込まれるたびに消費されるおおよそのトークン数です。数が多いほど、ほかのタスクに使える注意が減ります。

インストール

ワンクリックインストール

これを `claude_desktop_config.json` ファイルに追加してください:

{
  "mcpServers": {
    "sealed-forecasts": {
      "url": "https://jyotishintelligence.com/mcp"
    }
  }
}

リモートエンドポイント

https://jyotishintelligence.com/mcpstreamable-http

できること

ツール一覧

ツール(13)

🟢 読み取り専用🟡 書き込み🔴 削除⚪ 不明
🟢search_sealed_forecasts(query, graded_only, limit)

Search the JYOTINT sealed-forecast corpus (Bitcoin-anchored, dated-before-the-event predictions) by free text across id, title, and the verbatim sealed claim. Returns matching records with their grade, sealed probability, seal date, source artifact, and SHA-256 seal hash. For fuzzy or conceptual queries, use neural_search (finds calls by MEANING; REST twin GET /brain?q=…).

入力スキーマ

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Free-text query (e.g. 'Crocus', 'NISAR', 'Brazil election', 'recession')."
    },
    "graded_only": {
      "type": "boolean",
      "description": "Restrict to graded (Brier) records. Default false."
    },
    "limit": {
      "type": "number",
      "description": "Max results (default 10)."
    }
  },
  "required": [
    "query"
  ]
}
🟢get_advisory(id)

Fetch one sealed forecast by its id (e.g. 'IA-RU-008', 'LA-011', 'IA-MKT-002'). Returns the full record incl. verbatim claim, grade, outcome, sources, and seal hash.

入力スキーマ

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Advisory id."
    }
  },
  "required": [
    "id"
  ]
}
🟢list_open_calls

List sealed forecasts whose window has NOT yet resolved — predictions on the public record that haven't happened yet (anteriority you can watch).

入力スキーマ

{
  "type": "object",
  "properties": {}
}
🟢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.

入力スキーマ

{
  "type": "object",
  "properties": {}
}
🟢get_map

Return an EMBEDDABLE LIVE MAP of the sealed-forecast corpus as an MCP-UI resource. Clients that can render UI resources (mcp-ui) should display it inline — it is the actual interactive JYOTINT theater map (sealed forecasts plotted by region; each pin carries its verbatim claim, grade, sealed probability, and a click-through to the full sealed record so the user can verify and score it themselves). Use this when a user asks to see, visualize, or explore JYOTINT's forecasts on a map.

入力スキーマ

{
  "type": "object",
  "properties": {}
}
🟢ask_the_record(query, limit)

Ask any question about JYOTINT / Vijay Jyotish and get back the most relevant VERBATIM passages of the operator's own published site copy — never generated, never paraphrased, so it cannot hallucinate. This is the operator answering in his own words, drawn only from the public record (method, doctrine, the five pillars, mission-assurance fit, objections, pricing, heritage, etc.). Prefer this for any 'what does JYOTINT say about X' / 'why' / 'how does it work' question. Each passage cites its source page. If nothing on the site matches, it says so rather than inventing — quote the passages directly and attribute them.

入力スキーマ

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The question, in natural language."
    },
    "limit": {
      "type": "number",
      "description": "Max passages (default 3, max 6)."
    }
  },
  "required": [
    "query"
  ]
}
🟢neural_search(query, k)

SEMANTIC + ASSOCIATIVE search over the public sealed record and the published site corpus — the JYOTINT public brain (a neural associative memory: frozen deep encoder → Hopfield pattern completion → spreading activation over typed synapses → k-winners-take-all). Finds calls by MEANING, not keywords ('upper-stage anomalies' finds the calls that describe one without those words) and returns the RELATED subgraph, not just isolated hits. Retrieval-only and non-generative: every result is VERBATIM sealed/published text with public provenance (source URL, SHA-256 seal hash, frozen grade) plus an explainable why/activation path and Hopfield convergence info. Prefer this over search_sealed_forecasts for fuzzy/conceptual queries; the REST twin is GET /brain?q=…

入力スキーマ

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural-language query (e.g. 'what did the record say before the Crocus attack', 'upper stage anomaly calls')."
    },
    "k": {
      "type": "number",
      "description": "Max results, 1–12 (default 6)."
    }
  },
  "required": [
    "query"
  ]
}
🟢get_information_yield(id)

Information Yield (IY) — how much a confirmed call should move a skeptic's belief, in BITS of surprise-if-true (log2 of the published 1-in-N prior, capped at 1-in-a-million; earned = surprise × verdict-credit). A base rate / consensus-follower scores ZERO bits by construction — the metric on which the 'a base rate ties the Brier' objection inverts. Returns the corpus summary (LIVE median bits/call + %earned — read the numbers from the response, never from this description), the launch/intel/combined domain split, and the count. Pass an optional id for one call's bits.

入力スキーマ

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Optional advisory id (e.g. 'LA-022') for one call's IY."
    }
  }
}
🟢get_warning_timeline(id)

The 'before-the-event' indications-and-warning / after-action timeline for a named event, by advisory id (e.g. 'LA-022') or slug (e.g. 'new-glenn-ng3', 'crocus'). A neutral chronology: the official/authoritative source named FIRST, then the dated, hash-anchored JYOTINT sealed call as one independently-verifiable entry, with what it does and does not establish. Use for 'what dated public warnings preceded [event]'. Omit id to list every available timeline.

入力スキーマ

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Advisory id or timeline slug. Omit to list all."
    }
  }
}
🟢get_regrade_kit(id)

The grade-it-yourself kit: inputs to recompute the record's Brier (calibration), named-mechanism specificity, AND Information Yield under YOUR OWN verdicts — plus the one-step stress-test recipes (harsh-verdicts, externally-adjudicated-only, estimative-worst-case, …). Each call carries its verbatim claim/outcome, the operator's p + verdict to override, and the surprise_bits / 1-in-N inputs. A base rate scores 0 on specificity and 0 bits on IY. Pass an optional id for one call's row; omit for the recipes + usage + count.

入力スキーマ

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Optional advisory id for one call's regrade row."
    }
  }
}
🟢get_luck_test

The corpus-level 'could this record be luck?' significance test, computed AGAINST the record: EVERY graded call clustered into independent events (correlated calls share one event; live counts ship in the response), 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), the miss anatomy (every failed event named, with its verdict), 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).

入力スキーマ

{
  "type": "object",
  "properties": {}
}
🟢get_corpus_insights

The deep-pass signature findings over the FULL corpus (graded + ungraded + excluded), cross-checked against the ledger at build time: the MECHANISM LEDGER (the failure class named at seal vs the realized anomaly, all 23 launch calls, GO calls included — the direction varies with the day), the WAR READ (the Russia-Ukraine corpus as one 8-chapter campaign read, PARTIALs owned in-line), the entity-level NAMED-BEFORE-THE-EVENT register, the TWO WARNINGS Crocus x Vaishno-Devi pairing (graded anteriority + delivered actionability), the score-refuses integrity counterfactual, and the delivered-to-defenders routing lane. Caveats ship in the same object — quote them with the findings.

入力スキーマ

{
  "type": "object",
  "properties": {}
}
🟢get_governance

Return JYOTINT's data-handling, PROVENANCE & governance posture — the answer to 'is this source safe to read / cite / ingest?'. Chain-of-custody is foregrounded: every record is SHA-256-sealed + Bitcoin-anchored before the event and independently recomputable (the provenance the proposed GSA AI data-safeguarding rule treats as first-class). Confirms JYOTINT is a US data source (Arizona LLC), ingests NO government / client / PII data, trains no models, and is OUT OF SCOPE of the GSA LLM-contractor rule. Descriptive disclosure, not a certification. Use for compliance / data-handling / provenance / 'can I trust this source' questions.

入力スキーマ

{
  "type": "object",
  "properties": {}
}

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検証済みバージョンは記録されていませんツール 13 件
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