MCP Server Stacking: One Data Source is Data, Two is Intelligence
Table of content
A single MCP server gives Claude access to one data source. Two servers give it the ability to correlate across domains. Three or more create a personal operating system where AI understands context that spans your entire digital life.
This guide shows how to stack MCP servers for compounding intelligence, with configuration patterns that actually work.
Why stacking matters
Each MCP server is isolated. Garmin MCP knows your sleep data. Strava MCP knows your workout performance. Neither knows about the other.
When you connect both to Claude, something new happens: the AI can correlate sleep quality with next-day performance. It can identify patterns you would never find manually.
| Single server | Stacked servers |
|---|---|
| “Your HRV was 45 yesterday” | “Your HRV dropped to 45 after three consecutive hard workout days” |
| “You rode 50km on Saturday” | “Your Saturday rides average 20% slower when sleep dips below 7 hours” |
| Raw data retrieval | Cross-domain pattern recognition |
Brian Christner’s Human OS demonstrates this with fitness data: stacking Garmin and Strava MCPs increased his PR attempt success rate from 35% to 71%. The servers individually could not predict readiness. Together, they could.
Architecture
Claude acts as the integration layer. Each MCP server exposes tools and resources through a standard protocol. Claude can call any combination of tools in a single response.
[You] → [Claude] → [MCP Server A] → External Service A
→ [MCP Server B] → External Service B
→ [MCP Server C] → External Service C
The key insight: servers do not communicate with each other. All correlation happens in Claude’s context window. This means you pay no engineering cost for integration between domains.
Configuration
Claude Desktop
Edit ~/Library/Application Support/Claude/claude_desktop_config.json on macOS:
{
"mcpServers": {
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxx"
}
},
"postgres": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-postgres"],
"env": {
"DATABASE_URL": "postgresql://user:pass@localhost/db"
}
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/dir"]
}
}
}
Claude Code
# Add servers individually
claude mcp add github --command "npx -y @modelcontextprotocol/server-github"
claude mcp add postgres --command "npx -y @modelcontextprotocol/server-postgres"
# Or edit ~/.claude/mcp.json directly for complex configs
Verify your stack:
claude mcp list
claude mcp status
Effective stacks
Developer workflow
GitHub MCP + Postgres MCP + Filesystem MCP
What this enables:
- “Find all PRs that touched the payments table”
- “Show me the schema for tables mentioned in this error”
- “Which config files affect the failing tests in this PR?”
Research and writing
Exa MCP + Obsidian MCP + Browser MCP
What this enables:
- Search the web, cross-reference with your notes, and save findings
- “What have I written about this topic, and what’s new since then?”
- “Summarize this article and link it to relevant notes in my vault”
Simon Willison’s workflow shows how tools like llm and datasette connect through SQLite. MCP stacking follows the same principle: small tools composed into larger capabilities.
Business intelligence
CRM MCP + Calendar MCP + Slack MCP
What this enables:
- “Prepare me for my 2pm call with Acme Corp”
- “Which clients haven’t responded in 30 days?”
- “Summarize all communication with this account before the renewal meeting”
Prompting stacked servers
Claude needs explicit instruction to use multiple servers together. Otherwise it may only query one.
| Weak prompt | Strong prompt |
|---|---|
| “What’s my schedule?” | “Check my calendar for today, then look up each meeting attendee in the CRM” |
| “How did I sleep?” | “Get my sleep data from Garmin and correlate it with yesterday’s Strava workout” |
The pattern: name the servers or data sources you want involved.
Common mistakes
| Mistake | Why it fails | Fix |
|---|---|---|
| Too many servers at once | Context window fills with tool descriptions, less room for actual reasoning | Start with 2-3 servers, add more as needed |
| Overlapping capabilities | Multiple servers that do the same thing confuse tool selection | Pick one server per domain |
| No explicit correlation request | Claude queries one server and stops | Prompt with “combine” or “correlate” or name both sources |
| Secrets in config file | Committing API keys to version control | Use environment variables, add config to .gitignore |
| Servers that require different runtimes | Python server mixed with Node server causes dependency confusion | Standardize on one runtime where possible, or use Docker |
Performance considerations
Each MCP server is a separate process. Ten servers means ten processes consuming memory. On a typical machine:
| Server count | Memory overhead | Startup time |
|---|---|---|
| 1-3 | Negligible | Instant |
| 4-7 | ~500MB | 2-3 seconds |
| 8+ | 1GB+ | 5+ seconds |
If you hit performance issues, remove servers you do not use daily. Add them back when needed for specific tasks.
Building your own stack
Week 1: Pick one domain (fitness, work, or research) and add two complementary servers. Ask questions that require both.
Week 2: Add a third server from a different domain. Practice prompts that span all three.
Week 3: Audit which servers you actually use. Remove dormant ones. Add new ones for gaps you notice.
The goal is not maximum servers. It is maximum insight per server.
Next: Building Your First MCP Server
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