FlightFix

io.github.everyai-comv1.0.0更新於 Oct 4, 2026

EU261/UK261 flight compensation check, rights and claim letters. Free.

已驗證Streamable HTTP可網頁執行Location & Lifestyle

概覽

AI 產生的概覽

檢查 EU261/UK261 航班賠償資格，並起草依據旅客權利的索賠信。

功能
FlightFix 是一個免費的遠端 MCP 端點，用於 EU261/UK261 航班賠償。它讓助理判斷受影響的航班是否符合賠償條件、說明該法規下的旅客權利，並產生素賠信。清單中未列出任何工具，因此具體工具名稱在此沒有說明。
適用情境
當使用者詢問延誤、取消或超賣航班是否符合歐盟或英國賠償條件、想了解自身權利，或需要起草索賠信時使用。它是用途單一的伺服器，而非通用的旅遊助理。
執行需求
遠端 streamable HTTP 端點 API 金鑰。需要能連線至該端點的網路。
安裝前請注意
登錄描述稱此服務免費，且未宣告任何憑證、付款或寫入操作。用於檢查的航班資訊會傳送到第三方端點，因此不要提供超出索賠所需的個人或訂位資料。清單未列出任何工具，依賴其輸出前請先確認此伺服器實際的用途。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 FlightFix，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "flight-fix": {
      "type": "http",
      "url": "https://flight-fix.magicteams.ai/mcp"
    }
  }
}

README

Sahadeva

[CI]

A clean-room, Cloudflare-native research build for a transparent South Indian AI Jyotish workspace.

Run locally

bash
npm installnpm run dev

The Cloudflare Vite plugin serves the React application and Worker API together. POST /api/chart is deterministic. POST /api/interpret uses the configured Workers AI binding.

AI narration

Narration uses only Cloudflare's native AI binding and Cloudflare-hosted @cf/... models. The default is @cf/zai-org/glm-5.3-flash; AI_MODEL selects it in wrangler.jsonc, and non-@cf/ model names are rejected in favor of that safe default. No AI Gateway, third-party API, or provider token participates in this path. GET /api/ai/status exposes only non-secret readiness metadata, and successful narration identifies the Cloudflare model while keeping calculated evidence immutable.

The consultation surface adds deterministic topic judgments, house exploration, natal Panchanga, convention comparison, Prashna, timing fusion, rectification, and depth endpoints. Topic judgments share one evidence-ledger engine across web, MCP, reports, and the Expo mobile client. Opt-in snapshots store versioned ledgers and a hashed follow-up secret—not names, places, or raw birth input—and calibration remains descriptive rather than predictive.

Connect through MCP

After deployment, compatible clients can connect to:

text
https://your-worker.example/mcp

The Streamable HTTP endpoint exposes a high-signal catalog led by a comprehensive first-reading dossier. The returned profile reference lets later consultations answer focused questions from the verified natal context without repeating the dossier. It also supports chart reports, Prashna, timing, separate North Indian Ashtakoota and South Indian ten-Porutham compatibility, Panchanga, Muhurta and visual reports. Specialist tools remain directly callable and are documented through an MCP resource. Chart calculation does not require a language model. Public calls are rate-limited; optional scoped Bearer keys provide metering and vault integration. See MCP integration examples.

Clients can call assess_prediction_readiness before interpretation to distinguish implemented calculation from reviewed rules and calibrated prediction. Lal Kitab currently provides source-linked structural inspection. All of its material is retained, including sensitive topics and remedies; reviewed claims can later use graduated caution-led disclosure, while unreviewed personalized output remains withheld. See Prediction quality and MCP gap audit.

The source-only analyze_lal_kitab workflow now converts natal placements to Lal Kitab fixed houses and returns locators for all relevant planet-house sections without publishing their unreviewed claims. The complete integration inventory is in Lal Kitab integration map.

The web chart surfaces the same fixed-house map, source locators, retained-sensitive-material caution, and an independent calculation-quality audit. MCP clients can use the lal_kitab_consultation prompt or compose the equivalent tools from the published workflow resource.

For evidence-first use across AI clients, the MCP now publishes sahadeva://prediction-quality and the evidence_first_prediction prompt. Reviewed-rule search, rights-aware passage discovery, separate-tradition comparison, per-claim auditing, versioned outcome capture and conservative validation reporting are available as typed tools. These improve traceability and abstention; they do not guarantee prediction accuracy or convert unreviewed traditions into validated knowledge.

The primary consultation also supports a compact cross-tradition whole-person profile and consent-aware remedy selection. Its security contract is published at sahadeva://security; production profile references are keyed opaque identifiers, restricted knowledge remains server-side, and retrieved text is explicitly untrusted data. See MCP AI orchestration and security.

Workers AI has a limited daily free allocation. Sahadeva therefore describes hosted narration as optional and allowance-backed, not unlimited free inference.

Verify

bash
npm run checknpm testnpm run buildnpm run test:e2enpm run migrations:verifynpm run mobile:verify

Current status

The product interface and computation pipeline are functional. The Moon, Panchanga transitions, and Lahiri convention have high-precision reference fixtures; the application remains a research preview until independent Lagna, solar-event, complete-strength, and practitioner certification gates are satisfied. See RESEARCH_AND_ARCHITECTURE.md for the calculation boundary, topology, and validation gates.

Clean-room boundary

No third-party astrology engine is bundled. Repository research informed the capability map and test strategy only. Production algorithms must be derived from documented specifications and independently validated.

Continuous integration

Pushes and pull requests run the verify pipeline on Flare Actions (flare.yml): typecheck, unit tests, migration and secret checks, audit, build, bundle budget, Playwright e2e, and the Expo mobile verify. Run completions email all registered users.

License

MIT — see LICENSE.

來源：README.md，提交 684fa11

工具

0
工具後設資料尚未被收錄。

版本歷史

1
  1. v1.0.0最新Oct 4, 2026