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Omega Point Convergence MCP Server

@apifyforge

代码仓库主页文档

基本信息

分类

其他

许可证

MIT

认证方式

oauth

作者

apifyforge

关于 Omega Point Convergence MCP Server

Technology convergence prediction is the primary use case for this MCP server — it analyses when and how separate technology domains will merge into unified frameworks, using data from 16 simultaneous sources.

Omega Point Convergence MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "omega-point-convergence-mcp": {
      "url": "https://ryanclinton--omega-point-convergence-mcp.apify.actor/mcp"
    }
  }
}

Omega Point Convergence MCP Server 工具

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概览

What is Omega Point Convergence MCP Server?

An MCP server that predicts when and how separate technology domains will converge, using data from 16 simultaneous sources and eight independent mathematical algorithms. It is designed for technology strategists, R&D leaders, and venture investors who need quantitative convergence probability, timeline, and phase classification.

How to use Omega Point Convergence MCP Server?

Add the server URL to your MCP client (Claude Desktop, Cursor, Windsurf) using the endpoint https://ryanclinton--omega-point-convergence-mcp.apify.actor/mcp. No API key is needed in the connection URL. Then call tools like forecast_omega_point_timing with a technology name and optional depth ("standard" or "deep").

Key features of Omega Point Convergence MCP Server

  • 16 parallel data sources including patents, papers, code, and grants
  • Eight independent algorithms: CW homology, tropical geometry, Ricci flow, and more
  • Composite scoring with ±0.15 confidence interval
  • Pay-per-event pricing with spending limit detection
  • Standby mode for low-latency responses
  • Scheduling, monitoring, and integrations via Apify platform

Use cases of Omega Point Convergence MCP Server

  • R&D planning: identify technology domains on collision courses to redirect research
  • Venture capital timing: determine sector phase (embryonic, growth, saturation) for ideal entry
  • Academic direction: map citation flow dynamics via Hodge decomposition to find unsettled territory
  • Competitive intelligence: map Betti numbers to reveal white spaces and IP moats
  • Biotech pipeline assessment: apply readiness assessment to drug modalities using multi-source data

常见问题

1

What data sources does it use?

It orchestrates 16 Apify actors: USPTO, EPO, OpenAlex, Semantic Scholar, arXiv, DBLP, CORE, GitHub, Stack Overflow, Hacker News, Finnhub, CoinGecko, NIH Grants, Grants.gov, ClinicalTrials.gov, and Data.gov.

2

How is the convergence probability calculated?

The server runs eight algorithms independently, then synthesises results using a composite score weighting topological complexity, S-curve phase, and stratified gradient to produce a single probability with a ±0.15 confidence interval.

3

Do I need an API key to connect?

No API key is required in the connection URL; the Apify token is handled server-side by the platform.

4

What is the cost model?

Charging is per-tool pay-per-event with a configured spending limit; runs terminate cleanly when the budget is reached rather than producing partial results.

5

What depth options are available for analysis?

The `forecast_omega_point_timing` tool accepts `depth: "standard"` (75 results per source, faster and cheaper) or `depth: "deep"` (150 results per source, more data).

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