Tech Ecosystem Analysis
@apifyforge
Tech Ecosystem Analysis について
Tech ecosystem analysis for AI agents and LLM workflows — maps technology relationships, tracks adoption curves, detects CVE vulnerabilities, and scores tech stack risk across 6 live data sources.
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"tech-ecosystem-analysis-mcp": {
"url": "https://ryanclinton--tech-ecosystem-analysis-mcp.apify.actor/mcp"
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Tech Ecosystem Analysis?
Tech Ecosystem Analysis is an MCP (Model Context Protocol) server for AI agents and LLM workflows that maps technology relationships, tracks adoption curves, detects CVE vulnerabilities, and scores tech stack risk from six live data sources. It is intended for engineering teams, security analysts, and CTOs evaluating frameworks, languages, or tools with structured, data-driven intelligence.
How to use Tech Ecosystem Analysis?
Add the server’s URL and optionally an Authorization header (Apify token) to your MCP client configuration (Claude Desktop, Cursor, Windsurf). The server exposes 8 tools that can be called directly from an AI agent with a single tool invocation; example HTTP and Python usage are provided in the README.
Key features of Tech Ecosystem Analysis
- 8 MCP tools covering vulnerability, adoption, sentiment, and risk scoring
- Live data from GitHub, NVD, StackExchange, Hacker News, Wikipedia, and Tech Stack Detector
- Parallel execution of up to 6 actor calls per tool invocation
- Structured JSON output with scores, classifications, and recommendations
- Streamable HTTP transport – no SSE configuration required
- Apify platform features: scheduling, API access, spending limits, and integrations
Use cases of Tech Ecosystem Analysis
- Engineering teams comparing framework risk before committing resources
- Security engineers mapping CVEs and prioritizing remediation
- VC and M&A analysts assessing technology moat and liability
- DevRel teams identifying growing frameworks for content investment
- AI agents monitoring production stack for new vulnerabilities weekly
FAQ from Tech Ecosystem Analysis
What tools does Tech Ecosystem Analysis expose?
It provides 8 tools: map_tech_ecosystem, assess_tech_adoption, detect_tech_vulnerabilities, analyze_developer_sentiment, score_tech_stack_risk, track_framework_trends, assess_tech_maturity, and generate_tech_report. Each tool returns specific structured data.
What data sources are used?
The server queries GitHub Search, NVD CVE Database, StackExchange, Hacker News, Wikipedia, and the Tech Stack Detector (for live URL scanning). All data is fetched at query time, not from a cached snapshot.
How are the scoring algorithms computed?
Scores are 0–100 composites. For example, the Vulnerability Exposure Score uses weighted severity (critical×3, high×2, other×1), capped at 100. The Tech Stack Risk Score combines CVE exposure (40%), inverse community support (35%), and inverse maturity (25%). Full formulas are documented.
Do I need an Apify account and token?
Yes. The server runs on the Apify platform. You must provide an Apify API token in the Authorization header when connecting. A free or paid Apify account is required to generate a token.
What transport does the server use?
It uses Streamable HTTP transport – no SSE (Server-Sent Events) configuration needed. The endpoint accepts standard HTTP POST requests.
「その他」の他のコンテンツ
MCP Go 🚀
mark3labsA Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.
🚀 Model Context Protocol (MCP) Curriculum for Beginners
microsoftThis open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
Production-ready MCP integrations for AI applications
Klavis-AIKlavis AI: MCP integration platforms that let AI agents use tools reliably at any scale
Inbox Zero AI
elie222The world's best AI personal assistant for email. Open source app to help you reach inbox zero fast.

Sequential Thinking
modelcontextprotocolModel Context Protocol Servers
コメント