Startup Ecosystem Intelligence
@apifyforge
About Startup Ecosystem Intelligence
Startup ecosystem intelligence for VC deal sourcing gives your AI assistant instant access to 8 public data sources — patents, GitHub activity, job postings, ArXiv research, tech stacks, corporate registries, and SaaS competitive data — all fused into a single structured deal mem
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"startup-ecosystem-intelligence-mcp": {
"url": "https://ryanclinton--startup-ecosystem-intelligence-mcp.apify.actor/mcp"
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Startup Ecosystem Intelligence?
Startup Ecosystem Intelligence is an MCP server that gives AI assistants instant access to 8 public data sources — patents, GitHub activity, job postings, ArXiv research, tech stacks, corporate registries, and SaaS competitive data — fused into a single structured deal memo. It is built for venture capitalists, corporate development teams, and accelerator managers who need quantified, behavior-based signals rather than self-reported pitch deck data.
How to use Startup Ecosystem Intelligence?
Add the MCP endpoint https://startup-ecosystem-intelligence-mcp.apify.actor/mcp to your MCP client (Claude Desktop, Cursor, Windsurf, or Cline) with your Apify API token as the Bearer token. Then ask your AI assistant to run a deal memo (e.g., "Generate a deal memo for Cohere") or a targeted analysis. The server queries up to 8 data sources in parallel and returns structured JSON with scores, signals, and red flags in 60‑90 seconds.
Key features of Startup Ecosystem Intelligence
- Eight specialized MCP tools for targeted or full‑memo analysis
- Parallel data collection using
Promise.allSettled()— no single source blocks results - Innovation Velocity Score (0–100) with five velocity levels
- Hiring Signal Decoder infers strategic direction from job postings
- Competitive Moat Analyzer scores tech stack, patents, and market density
- Corporate Health Check scores entity status, jurisdiction, and complexity
- Composite deal rating engine (PASS / WATCH / DILIGENCE / STRONG_BUY)
- Automatic red flag detection and investment thesis generation
Use cases of Startup Ecosystem Intelligence
- VC deal sourcing: triage 40–80 inbound decks per week with a 90‑second automated screen
- Corporate development: map IP landscapes and acquisition targets weekly
- Accelerator portfolio benchmarking: compare portfolio companies against cohort signals
- Technology trend scouting: quantify momentum in a technology area for thesis building
- Pre‑investment due diligence: verify corporate structure across 140+ registries before legal review
FAQ from Startup Ecosystem Intelligence
What data sources does it use?
It uses 8 public data sources: OpenCorporates (140+ jurisdictions), USPTO and EPO patent searches, GitHub repo search, Website Tech Stack Detector, Job Market Intelligence, ArXiv preprint search, and SaaS Competitive Intelligence.
How long does a full deal memo take?
A full deal memo takes about 60–90 seconds because up to 8 Apify actors fire in parallel; a single failing data source does not block the entire analysis.
What scoring models are applied?
Four scoring algorithms are applied: Innovation Velocity Score (0–100), Hiring Signal Decoder, Competitive Moat Analyzer, and Corporate Health Check, which are combined into a composite deal rating.
Does it require a Crunchbase subscription?
No. It uses only public data sources and Apify actors — no Crunchbase subscription or self‑reported founder data is needed.
How do I connect it to my MCP client?
Add the endpoint URL and your Apify API token as a Bearer header to your MCP client configuration. Examples for Claude Desktop, Cursor, Windsurf, and direct HTTP calls are provided in the README.
More Data & Analytics MCP servers
mcp-simple-arxiv
andybrandtTool to work with arXiv, provide LLM with ability to search and read papers from there
MCP From Zero: Quick Data
dislerPrompt focused MCP Server for .json and .csv agentic data analytics for Claude Code
MCP Deep Web Research Server (v0.3.0)
qpd-vEnhanced MCP server for deep web research
ArXiv MCP Server
blazickjpA Model Context Protocol server for searching and analyzing arXiv papers
HubSpot MCP Server
peakmojoA Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data, providing built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.
Comments