官方说明7 个来源MCP 服务8 天前
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
在 GitHub 查看 ↗直链 · 无中转页
配置模板（请按你的客户端调整）
claude mcp add scout-intel-mcp -- npx -y scout-intel-mcp
这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。
- ★ 数取值
- 2 ★ · omniologynow-rgb/scout-intel-mcp · 取自 GitHub 搜索 API · （2026-10-16 20:00:20 核对）
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
- 评分构成
- 综合分 703 · 2 ★ · 7 个来源 · mcp
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
omniologynow-rgb/scout-intel-mcp；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
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
- 来源
- awesome · https://github.com/omniologynow-rgb/scout-intel-mcp
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