Content
# MCP Servers & Tools Analysis
Based on the information provided, I'll analyze the MCP (Model Context Protocol) servers and tools, organize them into categories, and create a checklist for their features. Then I'll outline how to build a website analyzer with memory for bambisleep using the specified tools.
## MCP Servers & Tools Categories
### 1. Data Storage & Retrieval
- **Milvus MCP Server** - Vector database for similarity search
- **Memory MCP** - Persistent memory for conversations
- **Filesystem MCP** - Local file system access
### 2. Web & Information Access
- **Fetch MCP** - Web page content retrieval
- **DuckDuckGo MCP** - Web search capabilities
- **Puppeteer MCP** - Browser automation and scraping
- **GitHub MCP** - GitHub repository access
### 3. Communication & Integration
- **Discord MCP** - Discord interaction
- **Stripe MCP** - Payment processing integration
### 4. AI & Computation
- **SmollM2** - Small language model
## Feature Checklist for MCP Servers
### Milvus MCP Server
- [ ] Connection management
- [ ] URI configuration
- [ ] Authentication handling
- [ ] Database selection
- [ ] Collection management
- [ ] List collections
- [ ] Create collection with schema
- [ ] Load collection to memory
- [ ] Release collection from memory
- [ ] Get collection info
- [ ] Search operations
- [ ] Vector similarity search
- [ ] Text search
- [ ] Query with filters
- [ ] Data operations
- [ ] Insert data
- [ ] Delete entities
### Memory MCP
- [ ] Store conversation history
- [ ] Retrieve previous interactions
- [ ] Maintain context across sessions
- [ ] Clear/manage stored memory
### Filesystem MCP
- [ ] Read files
- [ ] Write files
- [ ] List directory contents
- [ ] Check file existence
- [ ] Get file metadata
### Fetch MCP
- [ ] Retrieve web page content
- [ ] Handle HTTP status codes
- [ ] Extract text content
- [ ] Parse HTML structure
- [ ] Follow redirects
### DuckDuckGo MCP
- [ ] Execute web searches
- [ ] Filter search results
- [ ] Extract snippets
- [ ] Handle pagination
- [ ] Different search types (web, images, news)
### Puppeteer MCP
- [ ] Browser automation
- [ ] Screenshot capture
- [ ] DOM interaction
- [ ] Form filling
- [ ] JavaScript execution
- [ ] Waiting for page elements
### GitHub MCP
- [ ] Repository access
- [ ] Code retrieval
- [ ] Issue tracking
- [ ] Pull request information
- [ ] User information
### Discord MCP
- [ ] Send messages
- [ ] Read channel messages
- [ ] Manage server members
- [ ] Handle events
- [ ] Role management
### Stripe MCP
- [ ] Payment processing
- [ ] Customer management
- [ ] Subscription handling
- [ ] Invoice retrieval
- [ ] Transaction history
## BambiSleep Website Analyzer Project
Here's how to create a website analyzer with memory using DuckDuckGo as search index and Milvus as the vector database:
### System Architecture
1. **Search & Data Collection**
- Use DuckDuckGo MCP to search for "bambisleep" related content
- Use Fetch MCP to retrieve full webpage content
- Use Puppeteer MCP for dynamic content extraction
2. **Data Processing & Storage**
- Extract relevant text from webpages
- Convert text into vector embeddings
- Store embeddings and metadata in Milvus
- Use Memory MCP to track processing state
3. **Analysis & Query Interface**
- Vector similarity search to find related content
- Question answering against the stored information
- Conversation history with Memory MCP
### Implementation Steps
1. **Set up Milvus MCP Server**
```bash
# Install and run Milvus (using Docker)
docker run -d --name milvus_standalone -p 19530:19530 -p 9091:9091 milvusdb/milvus:latest standalone
# Configure MCP server for Milvus
mkdir -p .cursor
```
2. **Create MCP configuration**
```json
{
"mcpServers": {
"milvus": {
"command": "uv",
"args": [
"--directory",
"/path/to/mcp-server-milvus/src/mcp_server_milvus",
"run",
"server.py",
"--milvus-uri",
"http://127.0.0.1:19530"
]
},
"duckduckgo": {
"image": "mcp/duckduckgo:latest"
},
"fetch": {
"image": "mcp/fetch:latest"
},
"memory": {
"image": "mcp/memory:latest"
},
"puppeteer": {
"image": "mcp/puppeteer:latest"
}
}
}
```
3. **Create a collection in Milvus for BambiSleep data**
```python
# Schema for BambiSleep data
collection_schema = {
"fields": [
{"name": "id", "type": "VARCHAR", "is_primary": True, "max_length": 100},
{"name": "url", "type": "VARCHAR", "max_length": 500},
{"name": "title", "type": "VARCHAR", "max_length": 200},
{"name": "content", "type": "VARCHAR", "max_length": 65535},
{"name": "summary", "type": "VARCHAR", "max_length": 1000},
{"name": "vector", "type": "FLOAT_VECTOR", "dim": 1536},
{"name": "source", "type": "VARCHAR", "max_length": 100},
{"name": "retrieved_at", "type": "VARCHAR", "max_length": 50}
]
}
```
4. **Implement the search and data collection process**
- Search for "bambisleep" related terms using DuckDuckGo
- Fetch full content of relevant pages
- Use Puppeteer for dynamic content if needed
- Store metadata and vectors in Milvus
5. **Create analysis functions**
- Extract key information about BambiSleep
- Analyze website structure, content types, and themes
- Identify related products, services, and communities
6. **Build query interface**
- Vector search to find similar content
- Question answering against the stored information
- Use Memory MCP to maintain conversation context
This implementation would allow for comprehensive information gathering about BambiSleep, with the ability to query the vector database for specific information and maintain context across multiple interactions. The system could be extended to periodically update the database with new information as it becomes available online.
