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{
"name": "mcp-integrations",
"description": "Integration guides and setup for MCP servers (Database, Vector DB, Cloud Providers, Docker/K8s, GitHub)",
"version": "1.0.0",
"author": {
"name": "Brock"
},
"commands": [
"./commands"
]
}

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# mcp-integrations
Integration guides and setup for MCP servers (Database, Vector DB, Cloud Providers, Docker/K8s, GitHub)

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# MCP Server Integration Guide
Complete guide for setting up Model Context Protocol (MCP) servers with Claude Code.
## What are MCP Servers?
MCP servers provide Claude Code with direct access to external systems and data sources. They enable real-time interaction with databases, cloud providers, Docker/Kubernetes, and more.
## Available MCP Servers
### 1. Database MCP
Connect to SQL and NoSQL databases for real-time queries and schema exploration.
### 2. Vector Database MCP
Integrate with Pinecone, Weaviate, or Qdrant for AI/ML workflows.
### 3. Cloud Provider MCPs
Direct integration with AWS, GCP, and Azure services.
### 4. Docker/Kubernetes MCP
Manage containers and clusters directly from Claude Code.
### 5. GitHub MCP
Enhanced Git operations, PR reviews, and issue management.
## Installation
### Prerequisites
```bash
# Node.js 18+ required
node --version
# Claude Code CLI
claude --version
```
### General MCP Setup
1. Install MCP server package:
```bash
npm install -g @modelcontextprotocol/server-{name}
```
2. Configure in Claude Code settings:
```json
{
"mcpServers": {
"server-name": {
"command": "mcp-server-name",
"args": ["--config", "path/to/config.json"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
```
3. Restart Claude Code to load the MCP server
## Database MCP Setup
### PostgreSQL
```bash
# Install
npm install -g @modelcontextprotocol/server-postgres
# Configure (~/.claude/config.json)
{
"mcpServers": {
"postgres": {
"command": "mcp-server-postgres",
"args": [],
"env": {
"DATABASE_URL": "postgresql://user:pass@localhost:5432/dbname"
}
}
}
}
```
**Usage**:
```
/query SELECT * FROM users WHERE email = 'test@example.com'
/schema users
/indexes users
```
### MongoDB
```bash
# Install
npm install -g @modelcontextprotocol/server-mongodb
# Configure
{
"mcpServers": {
"mongodb": {
"command": "mcp-server-mongodb",
"env": {
"MONGODB_URI": "mongodb://localhost:27017/mydb"
}
}
}
}
```
**Usage**:
```
/find users {"email": "test@example.com"}
/aggregate users [{"$group": {"_id": "$role", "count": {"$sum": 1}}}]
```
## Vector Database MCP Setup
### Pinecone
```bash
# Install
npm install -g @modelcontextprotocol/server-pinecone
# Configure
{
"mcpServers": {
"pinecone": {
"command": "mcp-server-pinecone",
"env": {
"PINECONE_API_KEY": "your-api-key",
"PINECONE_ENVIRONMENT": "us-east-1-aws"
}
}
}
}
```
**Usage**:
```
/vector-search "machine learning concepts" --top-k 5
/upsert-vectors [{"id": "vec1", "values": [...], "metadata": {...}}]
/delete-vectors ["vec1", "vec2"]
```
## Cloud Provider MCP Setup
### AWS
```bash
# Install
npm install -g @modelcontextprotocol/server-aws
# Configure
{
"mcpServers": {
"aws": {
"command": "mcp-server-aws",
"env": {
"AWS_ACCESS_KEY_ID": "your-access-key",
"AWS_SECRET_ACCESS_KEY": "your-secret-key",
"AWS_REGION": "us-east-1"
}
}
}
}
```
**Usage**:
```
/aws s3 ls s3://my-bucket
/aws ec2 describe-instances --filters "Name=tag:Environment,Values=production"
/aws lambda invoke my-function --payload '{"key": "value"}'
/aws rds describe-db-instances
```
### GCP
```bash
# Install
npm install -g @modelcontextprotocol/server-gcp
# Configure
{
"mcpServers": {
"gcp": {
"command": "mcp-server-gcp",
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service-account.json",
"GCP_PROJECT_ID": "my-project"
}
}
}
}
```
**Usage**:
```
/gcp compute instances list
/gcp storage buckets list
/gcp sql instances list
```
## Docker/Kubernetes MCP Setup
### Docker
```bash
# Install
npm install -g @modelcontextprotocol/server-docker
# Configure
{
"mcpServers": {
"docker": {
"command": "mcp-server-docker",
"args": ["--socket", "/var/run/docker.sock"]
}
}
}
```
**Usage**:
```
/docker ps
/docker images
/docker logs container-name --tail 100
/docker exec container-name ls -la
/docker stats
```
### Kubernetes
```bash
# Install
npm install -g @modelcontextprotocol/server-kubernetes
# Configure
{
"mcpServers": {
"k8s": {
"command": "mcp-server-kubernetes",
"env": {
"KUBECONFIG": "~/.kube/config"
}
}
}
}
```
**Usage**:
```
/kubectl get pods -n production
/kubectl describe deployment myapp
/kubectl logs deployment/myapp --tail=100
/kubectl get services
/kubectl top pods
```
## GitHub MCP Setup
```bash
# Install
npm install -g @modelcontextprotocol/server-github
# Configure
{
"mcpServers": {
"github": {
"command": "mcp-server-github",
"env": {
"GITHUB_TOKEN": "ghp_your_token_here"
}
}
}
}
```
**Usage**:
```
/gh pr list --repo owner/repo
/gh pr view 123
/gh pr review 123 --comment "LGTM"
/gh issue create --title "Bug" --body "Description"
/gh workflow run ci.yml
```
## Security Best Practices
1. **Store credentials securely**:
```bash
# Use environment variables
export DATABASE_URL="postgresql://..."
# Or use a secrets manager
{
"mcpServers": {
"postgres": {
"command": "mcp-server-postgres",
"env": {
"DATABASE_URL": "${env:DATABASE_URL}"
}
}
}
}
```
2. **Use read-only access when possible**:
```sql
-- Create read-only database user
CREATE USER readonly_user WITH PASSWORD 'secure_password';
GRANT CONNECT ON DATABASE mydb TO readonly_user;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO readonly_user;
```
3. **Limit permissions**:
- Use least privilege principle
- Create service accounts with minimal permissions
- Regularly rotate credentials
4. **Monitor usage**:
- Log all MCP operations
- Set up alerts for unusual activity
- Review access logs regularly
## Troubleshooting
### MCP Server Not Starting
```bash
# Check if server is installed
which mcp-server-postgres
# Test server manually
mcp-server-postgres
# Check logs
tail -f ~/.claude/logs/mcp-server-name.log
```
### Connection Issues
```bash
# Test database connection
psql postgresql://user:pass@localhost:5432/dbname
# Test API credentials
curl -H "Authorization: Bearer $API_KEY" https://api.provider.com/test
```
### Permission Errors
- Verify credentials are correct
- Check IAM roles/permissions
- Ensure firewall allows connections
- Verify network connectivity
## Creating Custom MCP Servers
### Basic MCP Server Template
```typescript
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
const server = new Server(
{
name: 'my-custom-server',
version: '1.0.0',
},
{
capabilities: {
tools: {},
},
}
);
// Define tools
server.setRequestHandler('tools/list', async () => {
return {
tools: [
{
name: 'my_tool',
description: 'Description of what this tool does',
inputSchema: {
type: 'object',
properties: {
param1: {
type: 'string',
description: 'Parameter description',
},
},
required: ['param1'],
},
},
],
};
});
// Handle tool calls
server.setRequestHandler('tools/call', async (request) => {
const { name, arguments: args } = request.params;
if (name === 'my_tool') {
const result = await doSomething(args.param1);
return {
content: [
{
type: 'text',
text: JSON.stringify(result, null, 2),
},
],
};
}
throw new Error(`Unknown tool: ${name}`);
});
// Start server
const transport = new StdioServerTransport();
await server.connect(transport);
```
## Best Practices
1. **Test MCP servers locally before deploying**
2. **Use version control for MCP configurations**
3. **Document custom MCP servers**
4. **Monitor performance and errors**
5. **Keep MCP servers updated**
6. **Implement proper error handling**
7. **Use connection pooling for databases**
8. **Cache frequently accessed data**
9. **Set appropriate timeouts**
10. **Log operations for debugging**
## Example Workflows
### Database Query & Analysis
```
1. /query SELECT * FROM users WHERE created_at > NOW() - INTERVAL '7 days'
2. Analyze results
3. /query UPDATE users SET status = 'active' WHERE ...
4. Verify changes
```
### Cloud Deployment
```
1. /docker build -t myapp:latest .
2. /docker push myapp:latest
3. /kubectl set image deployment/myapp myapp=myapp:latest
4. /kubectl rollout status deployment/myapp
```
### AI/ML Workflow
```
1. Generate embeddings for documents
2. /vector-search "relevant query" --top-k 10
3. Use results in RAG pipeline
4. /upsert-vectors with new documents
```

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