InvoiceGen

io.github.everyai-comv1.0.0更新于 Oct 1, 2026

Freelancer invoices with totals, tax, due dates. Free.

已验证Streamable HTTP可网页运行Business & Commerce

概览

AI 生成的概览

一个远程 MCP 端点，用于生成包含合计、税费和到期日的自由职业者发票。

功能
InvoiceGen 是一个托管型 MCP 服务器，可让助手生成自由职业者发票，包括明细合计、税费和到期日。注册表描述称其免费使用。清单中未列出工具，因此此处不提供具体工具名称和参数说明。
适用场景
适合在计费或记账流程中，需要助手起草或整理带税费和付款期限的简单自由职业发票时使用。由于工具接口未公开，依赖前请先查阅该端点自身的文档。
运行要求
远程 Streamable HTTP 端点：
安装前请注意
该端点为远程服务且未声明身份验证，发送给它的内容会离开用户本机，且可能没有访问控制。发票内容通常包含客户名称、金额和税务信息；在提供商的数据处理方式明确之前，避免发送敏感的商业或个人数据。所提供材料中未描述付款或写入类操作。

安装

在 SourceWeft 中

  1. 打开 控制台中的 InvoiceGen，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": {
    "invoice-gen": {
      "type": "http",
      "url": "https://invoice-gen.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 a7d5243

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 1, 2026