Official description7 sourcesMCP servers8d ago
omniologynow-rgb/scout-intel-mcp
The Google for AI agents — give Claude the power to research any company, analyze competitors, and track market trends. 6 tools, 5+ data sources, confidence-scored JSON. pip install scout-intel-mcp
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Config template (adjust for your client)
claude mcp add scout-intel-mcp -- npx -y scout-intel-mcp
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
- Star count
- 2 ★ · omniologynow-rgb/scout-intel-mcp · read from the GitHub search API · (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list · type: MCP server
- Harvest history
- first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
- Score
- score 703 · 2 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
omniologynow-rgb/scout-intel-mcp；first catalogued 2026-10-09；last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.Third-party quality signals
Scout MCP
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Business & Market Intelligence for AI Agents
---
> **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**.
---
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
---
Quick Install
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
Cursor
Add to Cursor Settings > MCP:
VS Code (Copilot MCP)
Add to .vscode/mcp.json:
pip (self-hosted)
---
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** |
---
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** | .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
bash
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
dockerfile
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)"]
bash
Build and run
---
> **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**.
---
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
---
Quick Install
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
Cursor
Add to Cursor Settings > MCP:
VS Code (Copilot MCP)
Add to .vscode/mcp.json:
pip (self-hosted)
---
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** |
---
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** | .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
bash
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
dockerfile
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)"]
bash
Build and run
- Source
- awesome · https://github.com/omniologynow-rgb/scout-intel-mcp
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