Scouts-AI-MCP
Model Context Protocol server exposing the SCOUTS-AI web search API as a single web_search tool for AI agents. No API key required.
README
scouts-ai-mcp
Model Context Protocol (MCP) server that exposes the SCOUTS-AI web search API as a single web_search tool for AI agents, LLM apps, answer engines and GEO workflows.
- One tool, no API key. Backed by
GET https://scouts-ai.com/api/search. - Drop-in for Claude Desktop, Cursor, Open WebUI, Continue, Cline and any MCP host.
- Python ≥ 3.10,
fastmcpv2,httpx. - MIT licensed.
Install
pip install scouts-ai-mcp
Run (stdio)
scouts-ai-mcp
That's it. Wire it into your MCP host of choice — for example, Claude Desktop's claude_desktop_config.json:
{
"mcpServers": {
"scouts-ai": {
"command": "scouts-ai-mcp"
}
}
}
Run (HTTP)
For remote MCP hosts and self-hosted bridges:
scouts-ai-mcp --transport http --host 127.0.0.1 --port 8765
Run (Docker)
A multi-arch (linux/amd64 + linux/arm64) image is published to Docker
Hub as kecven/scouts-ai-mcp.
It runs the MCP server in streamable HTTP mode on port 8765 and exposes
the web_search tool at http://localhost:8765/mcp. No API key, no
external dependencies.
Quick start:
docker run --rm -p 8765:8765 kecven/scouts-ai-mcp:0.1.5
Then point any streamable-HTTP MCP host at http://localhost:8765/mcp
(or, when run behind a public proxy, https://<your-host>/mcp).
Override the upstream API base URL:
docker run --rm -p 8765:8765 \
-e SCOUTS_AI_BASE_URL=https://scouts-ai.com \
kecven/scouts-ai-mcp:0.1.5
Append CLI args (the image entrypoint is scouts-ai-mcp):
docker run --rm -p 8765:8765 kecven/scouts-ai-mcp:0.1.5 --log-level=DEBUG
Available tags:
kecven/scouts-ai-mcp:0.1.5— pinned, recommended for production.kecven/scouts-ai-mcp:0.1— minor-version rolling tag.kecven/scouts-ai-mcp:latest— latest stable release.
Build and push locally (requires docker buildx):
docker buildx build \
--platform linux/amd64,linux/arm64 \
-f Dockerfile.hosted \
-t kecven/scouts-ai-mcp:0.1.5 \
-t kecven/scouts-ai-mcp:0.1 \
-t kecven/scouts-ai-mcp:latest \
--push .
The image runs as a non-root user (scouts, uid 1001) and includes a
TCP-level HEALTHCHECK on 127.0.0.1:8765 (the streamable HTTP
endpoint does not return 200 on plain GET, so an HTTP probe would be
unreliable).
Tool: web_search
| Parameter | Type | Default | Description |
|---|---|---|---|
query |
string | — | Search query, 1–512 chars. |
lang |
string | en |
BCP-47 language code (e.g. en, en-US). |
page |
int | 1 |
1-based page number, 1–10. |
Returns a compact JSON object mirroring the SCOUTS-AI response shape:
{
"query": "rust async runtime",
"lang": "en",
"page": 1,
"pageSize": 10,
"cached": false,
"tookMs": 412,
"results": [
{
"title": "Tokio - An asynchronous runtime for Rust",
"url": "https://tokio.rs/",
"content": "Tokio is an asynchronous runtime for the Rust programming language...",
"publishedAt": "2025-11-14T00:00:00Z",
"engine": "duckduckgo"
}
]
}
Error handling
The tool raises ToolError (rendered as an MCP tool error) when:
- The query is empty/too long or
lang/pageare invalid → invalid arguments. - The upstream returns
429→ rate limit exceeded; honorsRetry-Afterwhen present. - The upstream returns
5xxor the network call fails → SCOUTS-AI temporarily unavailable. - The upstream returns a structured
4xxerror envelope → forwards the code and message.
Configuration
All settings are environment variables. Defaults match the public SCOUTS-AI deployment.
| Variable | Default | Description |
|---|---|---|
SCOUTS_AI_BASE_URL |
https://scouts-ai.com |
Base URL of the SCOUTS-AI API. |
SCOUTS_AI_TIMEOUT_S |
5.0 |
HTTP timeout in seconds (0.1–60). |
SCOUTS_AI_USER_AGENT |
scouts-ai-mcp/0.1.5 |
User-Agent header. |
SCOUTS_AI_DEFAULT_LANG |
en |
Default lang when the tool omits it. |
SCOUTS_AI_MAX_QUERY_LENGTH |
512 |
Reject queries longer than this. |
SCOUTS_AI_MAX_PAGE |
10 |
Reject page numbers above this. |
Development
git clone https://github.com/kecven/scouts-ai-mcp.git
cd scouts-ai-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest
License
MIT — see LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。