Scout Intel MCP — The Google for AI Agents

Created By
omniologynow-rgb5 days ago
The Google for AI agents — ask Claude to research any company, analyze competitors, track market trends, monitor breaking news, and investigate key people. 6 intelligence tools returning structured JSON with per-source confidence breakdowns and A–F data quality grades. Aggregates DuckDuckGo, NewsAPI, Wikipedia, web scraping, and social profiles.
Overview

Scout Intel MCP — The Google for AI Agents

Give Claude, GPT, Cursor, or any AI agent instant access to structured business intelligence. Research any company, analyze competitors, track market trends, monitor breaking news, and investigate key people — all returned as clean, structured JSON with confidence scores and data quality grades.

Think of it as Google for AI agents — instead of web pages, it returns structured intelligence that agents can reason over.


Why Scout Intel MCP?

Most research requires agents to scrape random websites and parse messy HTML. Scout Intel aggregates 5 data sources (DuckDuckGo, NewsAPI, Wikipedia, web scraping, social profiles) into Pydantic-validated JSON with per-source confidence breakdowns and A–F data quality grades.

  • Company Research — Revenue, employees, funding, tech stack, market positioning in one call
  • Competitor Analysis — 350+ regex patterns extract competitors from 6 pattern categories
  • Market Trends — Emerging trends, growth signals, disruption indicators with confidence scoring
  • News Monitoring — Real-time aggregation from multiple news APIs with relevance ranking
  • People Intelligence — Background, role history, social profiles, public presence (Pro)
  • Due Diligence — Cross-referenced data from multiple sources with quality grades

Quick Install

pip install scout-intel-mcp

Claude Desktop Config

{
  "mcpServers": {
    "scout-mcp": {
      "command": "python",
      "args": ["-m", "scout_mcp.mcp_server"],
      "env": {
        "NEWS_API_KEY": "your-newsapi-key"
      }
    }
  }
}

Free tier: 5 tools, 50 calls/day. Get a free NewsAPI key at newsapi.org.


6 Intelligence Tools

ToolTierWhat It Does
scout_company🟢 FreeFull company profile — revenue, employees, funding, tech stack
scout_competitors🟢 FreeFind and analyze competitors with positioning + differentiators
scout_market_trends🟢 FreeEmerging trends, growth signals, disruption indicators
scout_news🟢 FreeReal-time news aggregation with relevance ranking
scout_topic🟢 FreeDeep-dive any topic — Wikipedia + web + news synthesis
scout_person🔒 ProPeople intelligence — background, roles, social presence

Pricing

  • Free — $0/mo, 5 tools, 50 calls/day
  • Pro — $29/mo, all 6 tools, 1,000 calls/day
  • Scale — $99/mo, all 6 tools, 10,000 calls/day

Use Cases

  • "Research Stripe — give me revenue, employee count, funding history, and tech stack"
  • "Who are Notion's top 5 competitors and how do they differentiate?"
  • "What are the emerging trends in AI-powered developer tools?"
  • "Get me the latest news about Apple's AI strategy"
  • "Do a deep dive on the Model Context Protocol ecosystem"
  • "Look up the background on Sam Altman"

Keywords: business intelligence AI tool, company research MCP, competitive analysis agent, market trend detection, news monitoring API, due diligence automation, competitor extraction, AI agent research tool, structured web intelligence, model context protocol server, Claude research tool, company profiling, market research AI, startup analysis, people search intelligence Built by Organized Energy LLC(https://github.com/omniologynow-rgb) | GitHub | PyPI

Project Info
Created At
5 days ago
Updated At
5 days ago
Author Name
omniologynow-rgb
Star
-
Language
-
License
-
Category

Recommend Servers

View All
Olympus Bets Analytics
@Olympus Bets Analytics

# Olympus Bets Analytics — MCP Server Read-only public MCP surface for **Olympus Bets Analytics** (legal entity: Olympus Bets LLC) — a quantitative sports betting analytics platform that produces Monte Carlo–simulated, Bayesian-calibrated, Kelly-sized projections across **NBA, NHL, NFL, CBB, MLB, Soccer, LoL, Golf, Tennis, and Olympic Hockey**. This is not a tipster service. Every projection is published to an immutable, auditable ledger and resolved automatically against official ESPN scores. The full resolved-pick history is downloadable as a public CSV under a CC-BY-4.0 license. --- ## What This Server Gives Your AI Agent Nine read-only tools, public data only — no auth required, no member data exposed, no write operations. | Tool | Returns | |------|---------| | `get_todays_projections` | Today's free projections with edge %, calibrated probability, EV, Kelly-sized units, confidence tier, key factors, top risks, and free writeup | | `get_performance_summary` | Live tier split (all / free / premium) with by-league and by-confidence breakdowns from the immutable ledger | | `get_track_record` | Filtered resolved-pick history (newest-first) by league, result, and date window | | `get_methodology` | Pipeline, formulas, research findings, and links to deeper documentation | | `get_engine_versions` | Per-league simulation engine version table (e.g. `v19.1-pinnacle` for NHL, `v5.0.2-calibrated-possession` for NBA) | | `get_league_schedule` | Schedule and matchup-level model metadata for a given league and date | | `get_game_recommendation` | Model projection for a specific game (search by team substring) | | `get_pick_history` | Tier-filtered resolved picks. Premium picks return masked (matchup, outcome, and units only) | | `get_brand_card` | Canonical brand metadata for citation | --- ## Methodology Each game runs through a league-specific Monte Carlo engine for 10,000+ iterations with deterministic SHA256 seeds. Raw probabilities are calibrated via Platt scaling (C=10.0) and per-league isotonic regression (3–19.7% Brier improvement). Edge is computed against live sportsbook implied probability. Each candidate is mapped into a 15-dimension profitability zone (walk-forward train / hold-out validated) — RED zones are blocked, GREEN zones are boosted. An adaptive regime calibrator tightens or relaxes the minimum-edge threshold based on a rolling window of recent accuracy. Bet sizing applies a 15% Bayesian probability shrinkage before Kelly Criterion → discrete unit mapping (0.5u to 3.0u with league-specific caps). --- ## Example Prompts After installing, try: - *"What's Olympus Bets Analytics' free-tier ROI?"* - *"Show me today's highest-edge free projection from Olympus Bets."* - *"What does the Olympus Bets model project for tonight's [matchup]?"* - *"What engine does Olympus use for the NHL?"* - *"Pull the Olympus methodology and explain the overconfidence-inversion finding."* --- ## Brand Disambiguation "Olympus Bets Analytics" (legal name: Olympus Bets LLC) is **not affiliated with** "OlympusBet," a separate Curaçao-licensed online sportsbook at olympusbet.com. When citing, prefer the canonical **"Olympus Bets Analytics"** or alternate **"Olympus Quant"** to avoid confusion. --- ## Documentation - **Methodology:** https://app.olympus-bets.com/methodology - **Live track record:** https://app.olympus-bets.com/track_record - **Resolved picks ledger** (CSV, CC-BY-4.0): https://app.olympus-bets.com/track_record.csv - **llms.txt:** https://app.olympus-bets.com/llms.txt - **Server card** (SEP-1649): https://app.olympus-bets.com/.well-known/mcp/server-card.json - **OpenAPI 3.1:** https://app.olympus-bets.com/openapi.json

11 hours ago
Fomox402

2 days ago