scout-intel-mcp 1.0.1

Business & market intelligence MCP server for AI agents — 7 tools, 8 data sources, structured JSON with confidence scores

Scout MCP
Business & Market Intelligence for AI Agents

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Google for AI agents — instead of web pages, it returns clean, structured JSON that agents can reason over.

Scout MCP gives any AI agent instant access to structured business intelligence, market research, and competitive analysis. It aggregates data from DuckDuckGo, NewsAPI, Wikipedia, web scraping, and social profiles into Pydantic-validated JSON responses with per-source confidence breakdowns and data quality grades.

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Table of Contents

• Quick Install
• The 6 Intelligence Tools
• Data Quality Grades
• Confidence Breakdown
• Full API Reference
• Example Responses
• Architecture
• Configuration
• Pricing & Rate Limits
• Self-Hosting
• Docker
• Tech Stack
• Data Sources
• Contributing

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Quick Install

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

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

Cursor

Add to Cursor Settings > MCP:

{
  "scout-mcp": {
    "command": "python",
    "args": ["-m", "scout_mcp.mcp_server"],
    "cwd": "/path/to/scout-mcp/src",
    "env": {
      "NEWS_API_KEY": "your-newsapi-key"
    }
  }
}

VS Code (Copilot MCP)

Add to .vscode/mcp.json:

{
  "servers": {
    "scout-mcp": {
      "command": "python",
      "args": ["-m", "scout_mcp.mcp_server"],
      "cwd": "/path/to/scout-mcp/src",
      "env": {
        "NEWS_API_KEY": "your-newsapi-key"
      }
    }
  }
}

pip (self-hosted)

pip install scout-mcp
scout-mcp  # starts STDIO server for MCP clients

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The 6 Intelligence Tools

#	Tool	What It Does	Tier
1	scout_company	Structured intel on any company: industry, funding, tech stack, competitors, news, key people	Free
2	scout_market	Market research: size, CAGR, key players, trends, growth drivers, risks	Free
3	scout_competitors	Competitor analysis: positioning, pricing, strengths, weaknesses, differentiators	Free
4	scout_trends	Trend tracking: sentiment analysis, key developments, trending direction, related topics	Free
5	scout_product	Product intelligence: pricing, ratings, features, alternatives, recent updates	Free
6	scout_person	Public figure research: role, background, achievements, social profiles	Pro

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Data Quality Grades

Every response includes a data_quality_grade — a letter grade that lets agents instantly assess intelligence reliability:

Grade	Confidence	Meaning
A+	90%+	Exceptional — multiple high-quality sources confirmed
A	80-90%	High — strong multi-source corroboration
B	65-80%	Good — solid data from key sources
C	45-65%	Fair — limited sources, gaps likely
D	25-45%	Low — sparse data, treat with caution
F	<25%	Insufficient — minimal data available

How Agents Should Use Grades

result = scout_company("Stripe")

if result["data_quality_grade"] in ("A+", "A"):
    # High confidence — safe to make decisions on this data
    proceed_with_analysis(result)
elif result["data_quality_grade"] == "B":
    # Good but verify key claims
    proceed_with_caveats(result)
else:
    # C/D/F — supplement with additional sources
    request_more_data(result)

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Confidence Breakdown

Beyond the letter grade, every response includes a confidence_breakdown dict showing per-source reliability:

{
  "confidence": 0.86,
  "data_quality_grade": "A",
  "confidence_breakdown": {
    "duckduckgo": {
      "score": 0.60,
      "reason": "8 results found"
    },
    "company_website": {
      "score": 0.90,
      "reason": "scraped stripe.com, 3 data points extracted"
    },
    "wikipedia": {
      "score": 0.90,
      "reason": "page found, structured data extracted"
    },
    "newsapi": {
      "score": 0.90,
      "reason": "6 articles found"
    },
    "competitor_extraction": {
      "score": 0.80,
      "reason": "5 competitors identified"
    }
  }
}

Source Weights

Different sources carry different weights in the overall confidence calculation:

Source	Weight	Why
Wikipedia	3x	Curated, structured, authoritative
NewsAPI	2x	Fresh, professional journalism
Company Website	2x	First-party data, most current
DuckDuckGo Search	1x	Broad but variable quality
Competitor Extraction	1x	Derived analysis
Social Profiles	1x	Supplementary

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Full API Reference

Base URL

POST /api/scout/{tool_name}

Authentication

Pass your API key via the X-Api-Key header:

curl -X POST /api/scout/company \
  -H "X-Api-Key: your-key-here" \
  -H "Content-Type: application/json" \
  -d '{"name": "Stripe"}'

No key = free tier (50 requests/day).

Health & Info

Endpoint	Method	Description
/api/	GET	Service info + list of tools
/api/health	GET	Health check + DuckDuckGo backoff status
/api/tools	GET	All tools with params, tiers, and grade descriptions
/api/cache/stats	GET	Cache hit/miss statistics
/api/cache/clear	POST	Clear all cached responses

Tool Endpoints

POST /api/scout/company

Research any company.

Parameter	Type	Required	Description
name	string	Yes	Company name (e.g., "Stripe", "OpenAI")
domain	string	No	Company domain (e.g., "stripe.com"). Auto-detected if omitted.

Response fields: name, domain, description, industry, founded, headquarters, employee_range, funding, tech_stack, social_profiles, recent_news, top_competitors, key_people, confidence, confidence_breakdown, data_quality_grade, data_freshness, sources_used, sources_failed

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POST /api/scout/market

Research any market or industry.

Parameter	Type	Required	Description
query	string	Yes	Market to research (e.g., "AI SaaS", "electric vehicles")
depth	string	No	"summary" (default) or "detailed"

Response fields: market_name, query, depth, market_size, market_size_projections, cagr, key_players, trends, growth_drivers, risks, source_links, confidence, confidence_breakdown, data_quality_grade

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POST /api/scout/competitors

Find and analyze competitors.

Parameter	Type	Required	Description
company_or_product	string	Yes	Company or product name (e.g., "Notion")
max	integer	No	Max competitors to return (default 10)

Response fields: target, competitors (array of {name, domain, positioning, pricing, strengths, weaknesses, key_differentiator}), market_positioning_summary, confidence, confidence_breakdown, data_quality_grade

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POST /api/scout/trends

Track trends and sentiment.

Parameter	Type	Required	Description
topic	string	Yes	Topic to track (e.g., "generative AI")
timeframe	string	No	"1d", "7d" (default), "30d", "1y"

Response fields: topic, timeframe, sentiment ({score, label}), trending_direction, key_developments, related_topics, social_buzz, confidence, confidence_breakdown, data_quality_grade

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POST /api/scout/product

Get intelligence on any product.

Parameter	Type	Required	Description
name	string	Yes	Product name (e.g., "Slack", "Vercel")

Response fields: name, category, description, pricing, ratings, features, ideal_for, alternatives, recent_updates, confidence, confidence_breakdown, data_quality_grade

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POST /api/scout/person (PRO)

Research a public figure. Requires Pro tier or higher.

Parameter	Type	Required	Description
name	string	Yes	Person's name (e.g., "Sam Altman")
company	string	No	Company context (e.g., "OpenAI")

Response fields: name, current_role, company, location, background_summary, social_profiles, recent_activity, notable_achievements, confidence, confidence_breakdown, data_quality_grade

Returns HTTP 403 for free tier users.

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Example Responses

scout_company("Stripe")

{
  "name": "Stripe",
  "domain": "stripe.com",
  "description": "Stripe, Inc. is an Irish-American multinational financial services and software as a service company...",
  "industry": "Software & Technology",
  "founded": "2010",
  "headquarters": "San Francisco",
  "top_competitors": ["PayPal", "Adyen", "Square", "Braintree", "Checkout.com"],
  "tech_stack": ["Next.js", "React"],
  "recent_news": [
    {"headline": "Stripe launches AI billing features", "source": "TechCrunch", "date": "2026-04-08"},
    {"headline": "Stripe revenue grows 30% in 2025", "source": "Bloomberg", "date": "2026-03-15"}
  ],
  "confidence": 0.86,
  "data_quality_grade": "A",
  "confidence_breakdown": {
    "duckduckgo": {"score": 0.60, "reason": "8 results found"},
    "company_website": {"score": 0.90, "reason": "scraped stripe.com, 3 data points extracted"},
    "wikipedia": {"score": 0.90, "reason": "page found, structured data extracted"},
    "newsapi": {"score": 0.90, "reason": "6 articles found"},
    "competitor_extraction": {"score": 0.80, "reason": "5 competitors identified"}
  },
  "data_freshness": "2026-04-12T12:00:00Z"
}

scout_trends("generative AI", timeframe="7d")

{
  "topic": "generative AI",
  "timeframe": "7d",
  "sentiment": {"score": 0.72, "label": "positive"},
  "trending_direction": "up",
  "key_developments": [
    {
      "headline": "OpenAI releases GPT-5.2 with reasoning capabilities",
      "date": "2026-04-10",
      "impact_level": "high",
      "source": "The Verge"
    }
  ],
  "related_topics": ["Large Language Models", "AI Safety", "Enterprise AI"],
  "confidence": 0.83,
  "data_quality_grade": "A",
  "confidence_breakdown": {
    "duckduckgo_news": {"score": 0.90, "reason": "10 news articles found"},
    "newsapi": {"score": 0.90, "reason": "4 articles found"},
    "web_search": {"score": 0.50, "reason": "5 web results for context"}
  }
}

scout_competitors("Notion")

{
  "target": "Notion",
  "competitors": [
    {
      "name": "Obsidian",
      "domain": "obsidian.md",
      "positioning": "Privacy-focused local-first knowledge base with Markdown",
      "strengths": ["Open source", "Free tier available", "Offline support"],
      "key_differentiator": "Local-first with plain Markdown files"
    },
    {
      "name": "Coda",
      "positioning": "All-in-one doc with app-building capabilities",
      "strengths": ["AI-powered features"],
      "key_differentiator": "Document-as-app paradigm"
    },
    {
      "name": "Logseq",
      "positioning": "Open-source outliner with bidirectional links"
    }
  ],
  "market_positioning_summary": "Found 7 competitors for Notion. Top alternatives: Obsidian, Coda, Logseq, Anytype, AppFlowy.",
  "confidence": 0.65,
  "data_quality_grade": "B",
  "confidence_breakdown": {
    "search_Notion alternative": {"score": 0.70, "reason": "8 results for 'Notion alternatives'"},
    "search_Notion vs competi": {"score": 0.60, "reason": "6 results for 'Notion vs competitors'"},
    "extraction_quality": {"score": 0.86, "reason": "7 competitors extracted and enriched"}
  }
}

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Architecture

                      +------------------+
                      |  AI Agent        |
                      |  (Claude, etc.)  |
                      +--------+---------+
                               |
                    STDIO / SSE / REST
                               |
              +----------------+----------------+
              |          Scout MCP              |
              |  FastMCP 3.x + FastAPI REST     |
              +----------------+----------------+
              |  Cache (24h TTL, in-memory)     |
              |  Auth (API key, tier limits)     |
              |  Rate Limiter (per-key, daily)   |
              +----+--------+--------+----------+
                   |        |        |
          +--------+  +-----+--+  +--+--------+
          | DuckDuckGo| | NewsAPI | | Wikipedia |
          | (free)    | | (.org)  | | (free)    |
          +-----------+ +---------+ +-----------+
                   |                    |
          +--------+--------+  +-------+-------+
          | Web Scraper     |  | Social Profile |
          | (httpx + BS4)   |  | Detection      |
          +----------------+  +----------------+

Exponential Backoff (DuckDuckGo)

DuckDuckGo's free API has rate limits. Scout MCP handles this with adaptive backoff:

Success  : interval = 2s (base)
Failure 1: interval = 4s
Failure 2: interval = 8s
Failure 3: interval = 16s
Failure 4: interval = 32s
Failure 5: interval = 48s (cap)
Next success: interval resets to 2s

Each call also retries once before giving up. Monitor backoff status at GET /api/health.

Competitor Extraction Engine

The competitor extraction uses 6 regex pattern categories with 350+ stop words:

1. VS patterns — "X vs Y" matching
2. Comma/and-separated lists — "alternatives include X, Y, and Z"
3. Numbered/bulleted lists — "1. Asana 2. Monday 3. ClickUp"
4. Header patterns — "Asana -- project management tool"
5. Contextual patterns — "like X" or "such as X"
6. Title-cased names — capitalized product names near competitor context

Multi-word validators reject: article titles, verb-prefixed names, pronoun-prefixed names, role-suffixed names, probable person names, mega-corp parent names, and platform names.

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Configuration

Environment Variables

Variable	Required	Default	Description
NEWS_API_KEY	Yes	—	NewsAPI.org API key (get one free)
SCOUT_API_KEY	No	—	Master API key (auto-assigned "scale" tier)
MONGO_URL	Auto	—	MongoDB connection string (for REST API server)
DB_NAME	Auto	—	MongoDB database name
CORS_ORIGINS	No	*	Allowed CORS origins

Cache Settings

Responses are cached for 24 hours in-memory (dict). Cache stats and clear endpoints:

# Check cache stats
curl /api/cache/stats

# Clear all cache
curl -X POST /api/cache/clear

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Pricing & Rate Limits

Tier	Price	Daily Limit	Tools	Features
Free	$0/mo	50 requests	5 of 6	Summary depth, basic grades
Pro	$29/mo	1,000 requests	All 6	+ scout_person + detailed market reports
Scale	$99/mo	10,000 requests	All 6	Everything + priority support

Rate limit info is included in every response's _meta field:

{
  "_meta": {
    "tier": "free",
    "remaining": 47
  }
}

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Self-Hosting

Local Development

# Clone and install
git clone https://github.com/your-org/scout-mcp.git
cd scout-mcp/backend
pip install -e ".[dev,server]"

# Set up environment
echo "NEWS_API_KEY=your-key" > .env

# Run MCP server (STDIO for Claude Desktop)
cd src && python -m scout_mcp.mcp_server

# Run REST API server
uvicorn server:app --host 0.0.0.0 --port 8001 --reload

# Inspect with MCP Inspector
fastmcp inspect src/scout_mcp/mcp_server.py

Running Tests

# Test the API
curl -X POST http://localhost:8001/api/scout/company \
  -H "Content-Type: application/json" \
  -d '{"name": "OpenAI"}'

# Check health + backoff status
curl http://localhost:8001/api/health

# List all tools
curl http://localhost:8001/api/tools

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Docker

FROM python:3.12-slim
WORKDIR /app
RUN apt-get update && apt-get install -y --no-install-recommends gcc libxml2-dev libxslt1-dev && rm -rf /var/lib/apt/lists/*
COPY pyproject.toml .
COPY src/ src/
RUN pip install --no-cache-dir .
EXPOSE 8001
CMD ["python", "-c", "from scout_mcp.mcp_server import mcp; mcp.run(transport='sse', port=8001)"]

# Build and run
docker build -t scout-mcp .
docker run -p 8001:8001 -e NEWS_API_KEY=your-key scout-mcp

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Tech Stack

Component	Technology	Purpose
MCP Framework	FastMCP 3.x	Tool registration, STDIO/SSE transport
REST API	FastAPI	HTTP endpoints for testing
HTTP Client	httpx	Async web scraping
HTML Parser	BeautifulSoup4 + lxml	Structured data extraction
Search	DuckDuckGo (ddgs)	Free web + news search
News	NewsAPI.org	Professional news articles
Knowledge	Wikipedia API	Structured background data
Validation	Pydantic 2.x	Response model validation
Caching	In-memory dict (24h TTL)	Response caching
Server	uvicorn	ASGI production server

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Data Sources

Source	API Key?	Cost	Rate Limit	Reliability
DuckDuckGo Search	No	Free	Soft limits (backoff)	Variable
DuckDuckGo News	No	Free	Soft limits (backoff)	Variable
NewsAPI.org	Yes	Free tier	100 req/day	High
Wikipedia API	No	Free	Unlimited (polite)	Very High
Web Scraping (httpx)	No	Free	Per-site limits	Medium
Social Profile Detection	No	Free	Via DuckDuckGo	Variable

Future Sources (Planned)

• Crunchbase API (funding data)
• SimilarWeb API (traffic data)
• GitHub API (developer tools)
• SEMrush API (SEO data)

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-feature
3. Install dev dependencies: pip install -e ".[dev]"
4. Make your changes
5. Run tests: pytest
6. Submit a pull request

Adding a New Data Source

1. Create src/scout_mcp/sources/your_source.py
2. Implement async functions that return structured data
3. Add the source to relevant tools in src/scout_mcp/tools/
4. Add per-source confidence scoring
5. Update this README

Adding a New Tool

1. Create src/scout_mcp/tools/your_tool.py
2. Add a Pydantic model in models.py (include confidence_breakdown and data_quality_grade)
3. Register in mcp_server.py with @mcp.tool()
4. Add REST endpoint in server.py
5. Update this README

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License

MIT

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Built with FastMCP, httpx, BeautifulSoup4, Pydantic
Scout MCP v0.1.0