SSMCP
Provides web search with content extraction, YouTube subtitles, and optional LLM summarization for AI assistants.
README
SSMCP - Super Simple MCP Server
Model Context Protocol (MCP) server providing web search with content extraction.
Why Use SSMCP?
Many AI models, especially local models or certain cloud-based models, don't have built-in web browsing capabilities. SSMCP bridges that gap by providing a simple, self-hosted solution that gives your AI assistant the ability to:
- Search and Read the Web: Let your AI search for current information, read articles, documentation, or any web content
- Extract Clean Content: Automatically converts messy web pages into clean Markdown format that AI models can easily understand
- Access YouTube Transcripts: Extract subtitles from videos with timestamps for analysis or summarization
- AI-Powered Summarization: Optional LLM summarization extracts only relevant information from web pages based on your search query
- Privacy-Focused: Self-hosted solution - your searches and browsing stay on your infrastructure
- Works with Any Model: Compatible with local models (like Qwen, Llama) and cloud APIs (DeepSeek, Claude, GPT) that support MCP
Example Use Cases:
- Research recent news or developments on a topic
- Read and summarize technical documentation
- Analyze current market trends or product reviews
- Extract information from YouTube tutorials or presentations
- Get up-to-date answers that aren't in the model's training data
- Get concise, query-relevant summaries of web content (with LLM summarization enabled)
Features
- Web Search: Search the web and get results with extracted content in Markdown
- Web Fetch: Fetch and extract content from any URL as clean Markdown
- YouTube Subtitles: Extract subtitles and timestamps from YouTube videos
- LLM Summarization (Optional): Use an LLM to summarize and filter search results by relevance to your query
- Powered by: SearXNG for search, Crawl4AI for content extraction, yt-dlp for subtitles
- Simple API: Easy-to-use interface designed for compatibility with both small local models(like Qwen3 30b) and cloud models lacking web capabilities (e.g., DeepSeek 3.2)
- Container Support: Full containerized deployment with Docker Compose
Quick Start
Prerequisites
- Docker
- Docker Compose
1. Clone this repository
git clone https://github.com/antonsokolskyy/SSMCP.git
2. Create .env file
cd SSMCP/
cp .env.example .env
3. Set Up SearXNG
Start and stop the searxng container to generate settings.yml:
docker compose up searxng
Wait until you see:
"/etc/searxng/settings.yml" does not exist, creating from template...
Then press Ctrl+C to stop it.
Edit deploy/docker/searxng_data/settings.yml and add json to the formats list:
# remove format to deny access, use lower case.
# formats: [html, csv, json, rss]
formats:
- html
- json
4. Build the SSMCP image
docker compose build
5. Run the Full Stack
docker compose up -d
YouTube Cookies (Optional)
To access age-restricted or private YouTube videos, and to reduce the chances of hitting captchas or IP blocking, you can provide cookies from your browser.
Note: The cookies file must be in Netscape cookie format.
<details> <summary>Generating cookies.txt</summary>
Option 1: Using Browser Extension
Install an extension (like "Get cookies.txt LOCALLY") for your browser and export cookies for youtube.com in Netscape format.
Option 2: Using yt-dlp Binary
yt-dlp --cookies-from-browser chrome --cookies cookies.txt https://www.youtube.com/watch?v=VIDEO_ID
Replace chrome with your browser (firefox, edge, safari, etc.). This automatically exports in Netscape format.
</details>
<details> <summary>Using cookies.txt</summary>
Place the generated cookies.txt file in:
deploy/docker/ssmcp/cookies.txt
Security Warning: The cookies file contains authentication tokens and sensitive data. Set appropriate file permissions to prevent unauthorized access:
chmod 600 deploy/docker/ssmcp/cookies.txt
The file will be automatically detected and used by Docker container. </details>
MCP URL
The server uses Streamable HTTP transport. Connect to the MCP server at:
http://{HOST}:{PORT}/mcp
Example:
http://localhost:8000/mcp
Usage with MCP Clients
LM Studio
{
"mcpServers": {
"ssmcp": {
"url": "http://localhost:8000/mcp"
}
}
}
Web UI
SSMCP includes an optional Web UI for monitoring requests and responses. This is particularly useful for debugging and inspecting how the model uses the tools.
<details> <summary>Enabling the Web UI</summary>
- Configure Redis: Open your
.envfile and uncomment theREDIS_URLline:
REDIS_URL=redis://redis:6379
-
Enable Services: Open
docker-compose.yml(anddocker-compose.dev.ymlif using development mode) and uncomment theredisandssmcp-uiservice blocks. -
Restart Services:
make build
- Access the Monitor: Open http://localhost:8081 in your browser. </details>
Tools
web_search
Performs a web search and returns relevant results with extracted content.
Parameters:
query(str): Search query or keywords to find relevant web content
Returns:
- List of results, each containing:
url(str): The webpage URLcontent(str): Page content in Markdown format
web_fetch
Fetches content from a specified URL and converts it to Markdown.
Parameters:
url(str): The URL to fetch content from
Returns:
- String containing the page content in Markdown format
youtube_get_subtitles
Gets subtitles/captions from a YouTube video and returns the text content.
Parameters:
url(str): YouTube video URL to get subtitles from
Returns:
- String containing the subtitles with timestamps in format: [HH:MM:SS] text
How Search Works
SSMCP uses a pipeline to deliver clean content from web searches:
1. Search (SearXNG)
- Queries are sent to a local SearXNG instance
- SearXNG aggregates results from multiple search engines
- Returns a list of URLs with titles and snippets
2. Content Extraction (Crawl4AI)
- Each URL is fetched using headless Chromium browser
- Pages are fully rendered (JavaScript executed, dynamic content loaded)
- Raw content is extracted
- All URLs are processed concurrently
3. Content Filtering
Two-stage filtering extracts clean main content:
- CSS Selector Filter - Tries selectors (
article,main, etc.) to find main content area - Residual Junk Filter - Removes UI artifacts (tooltips, duplicate text)
If any filter produces output, the filtered HTML is re-processed through Crawl4AI for cleaner output before markdown conversion.
4. Markdown Conversion
- Converts filtered HTML to clean Markdown
- Removes images and external links
5. Optional: LLM Summarization
When enabled, SSMCP uses an LLM to summarize search results before returning them to your AI model. This reduces the token count in your context window - search results can be 40k-60k tokens, but summaries significantly reduce that.
Note: LLM summarization is disabled by default. Enable it via environment variables if needed (see .env.example).
Development
All development tasks are performed inside the Docker container. Nothing needs to be installed on the host machine except Docker and Docker Compose.
Available Make Commands
Run make help to see all available commands:
<details> <summary>Development Workflow</summary>
-
Enable Development mode:
Open.envand uncomment the lineCOMPOSE_FILE=docker-compose.yml:docker-compose.dev.yml -
Start the services:
make build -
Open a shell in the container:
make shellInside the shell, you can run any command:
uv run python -m ssmcp.server uv run pytest -v -
Edit code on your host machine - Changes are automatically reflected in the container via volume mounts
-
Run tests:
make test -
Check code quality (lint + type-check):
make check -
Restart or rebuild if needed:
make restart make rebuild -
Stop services:
make down
</details>
Configuration
All configuration is managed through environment variables. See .env.example for available options
OpenWebUI OAuth Authentication
SSMCP supports OAuth authentication for use with OpenWebUI and any OIDC-compliant identity provider.
How OpenWebUI OAuth Works
When you select OAuth in OpenWebUI for an MCP server:
- OpenWebUI forwards the system user's OAuth access token in the
Authorization: Bearer <token>header - The token is a JWT containing user information from the identity provider
- SSMCP validates the token and extracts the user identifier from the
subclaim
<details> <summary>Enabling OAuth</summary>
To enable OAuth authentication, set the following environment variables in your .env file:
# Enable OAuth authentication
OAUTH_ENABLED=true
# JWKS endpoint URL for your identity provider's public keys
# Examples:
# Authentik: https://authentik.example.com/application/o/my-app/jwks
OAUTH_JWKS_URL=https://your-idp.example.com/path/to/jwks
# Issuer URL for token issuer verification
# Must match the 'iss' claim in JWT tokens
# Examples
# Authentik: https://authentik.example.com/application/o/my-app
OAUTH_ISSUER=https://your-idp.example.com/application/o/my-app
# Open WebUI client ID for token audience verification
OAUTH_CLIENT_ID=your-openwebui-client-id
</details>
<details> <summary>Token Validation</summary>
When OAuth is enabled, SSMCP validates:
- JWT Signature: Verifies the token signature using the identity provider's public keys from the JWKS endpoint
- Issuer: Validates the
issclaim matchesOAUTH_ISSUER - Expiration: Validates the
expclaim - rejects expired tokens - Audience: Validates the
audclaim matchesOAUTH_CLIENT_ID - Subject: Requires the
subclaim (contains user identifier) </details>
<details> <summary>OpenWebUI Configuration</summary>
In OpenWebUI, configure the MCP server with:
- Type: MCP Streamable HTTP
- URL: Your SSMCP server URL (e.g.,
http://ssmcp:8000/mcp) - Auth: OAuth
- The system will automatically forward the user's OAuth token </details>
<details> <summary>Supported Identity Providers</summary>
SSMCP works with any OIDC-compliant identity provider that:
- Provides a JWKS endpoint for public key distribution
- Issues JWT access tokens with RS256 signing
- Includes standard claims (
sub,aud,exp,iss) </details>
License
Apache License 2.0 - see LICENSE file for details.
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。