PartyPlan

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

Party budgets, headcount quantities, timelines and checklists. Free.

已验证Streamable HTTP可网页运行Productivity & Workflow

概览

AI 生成的概览

用于派对预算、人数用量、时间表和清单的免费远程 MCP 服务。

功能
PartyPlan 是一个通过 Streamable HTTP 访问的托管 MCP 服务器，地址由服务方提供。其说明的用途是派对规划：预算、人数用量、时间表和清单。清单中未列出任何工具，因此具体工具集在此没有说明。
适用场景
适合让助手协助筹办活动时使用：估算费用、按宾客人数换算用量、制定时间表或清单。由于工具列表未公开，建议先在测试会话中试用再决定是否依赖。
运行要求
支持 Streamable HTTP 的远程 MCP 客户端，以及访问服务方端点的网络连接。未声明需要安装软件包、环境变量、请求头或身份验证。
安装前请注意
端点由第三方托管，因此你发送的活动信息（宾客人数、日期、预算数字）会离开本机。未声明任何身份验证，应视为公开服务，避免输入敏感的个人或财务信息。

安装

在 SourceWeft 中

  1. 打开 控制台中的 PartyPlan，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "party-plan": {
      "type": "http",
      "url": "https://party-plan.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.

来源：README.md，提交 684fa11

工具

0
工具元数据尚未被收录。

版本历史

1
  1. v1.0.0最新Oct 4, 2026