MCP vs Skills/Direct API Guide
Decision framework for choosing between MCP tools and direct API calls.
Quick Decision
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Need to call an external service? ├─ Is there a skill for it? → Use the skill ├─ Is cost control important? → Use direct API ├─ Need complex multi-step workflow? → Consider MCP └─ Simple one-off call? → Direct API is fine
When to Use Skills (Direct API)
Prefer skills/direct API when:
| Scenario | Why |
|---|---|
| Cost-sensitive services | Control model selection (Perplexity) |
| Simple queries | Less overhead than MCP |
| High-frequency calls | Faster, no tool discovery |
| Need predictable behavior | Skills have explicit instructions |
Services with skills:
| Service | Skill | Why not MCP |
|---|---|---|
| Perplexity | research | MCP defaults to expensive models |
| PostHog | analytics | Direct API is simpler |
email | Himalaya CLI, no MCP needed | |
| GitHub | github | gh CLI is more reliable |
When to Use MCP
Use MCP when:
| Scenario | Why |
|---|---|
| Complex multi-step workflows | MCP handles tool chaining |
| Need tool discovery | Don't know exact endpoint |
| Official MCP server exists | Better maintained |
| Service has many endpoints | MCP abstracts complexity |
Good MCP use cases:
| Service | MCP Server | Use Case |
|---|---|---|
| Apify | apify | Web scraping (many actors) |
| DataForSEO | dfs-mcp | SEO data (many endpoints) |
| Prospect | prospect | Lead enrichment |
| Google Workspace | google-mcp | Calendar, Sheets, Drive |
MCP Gotchas
1. Bad Defaults
Some MCP servers have expensive defaults:
| MCP | Issue | Solution |
|---|---|---|
| Perplexity MCP | Defaults to sonar-deep-research | Use research skill instead |
2. Token Overhead
MCP tool calls include:
- •Tool discovery
- •Schema validation
- •Extra context
For simple calls, this overhead can exceed the actual API call.
3. Startup Time
MCP servers (especially npx-based) have cold start delays:
- •First call: 5-15 seconds
- •Subsequent: Normal
Decision Matrix
| Factor | Use Skill | Use MCP |
|---|---|---|
| One endpoint | ✓ | |
| Many endpoints | ✓ | |
| Cost control needed | ✓ | |
| Complex workflow | ✓ | |
| Speed critical | ✓ | |
| Tool discovery needed | ✓ | |
| Explicit instructions exist | ✓ |
Available Skills
Check skills/ directory or use /list-skills for current skills.
| Skill | Purpose |
|---|---|
| research | Perplexity web search |
| analytics | PostHog queries |
| Email via Himalaya | |
| github | GitHub via gh CLI |
| lead-handler | Process incoming leads |
Available MCP Servers
Check workspace/mcporter.json for configured MCP servers.
Current servers:
- •
apify- Web scraping actors - •
dfs-mcp- DataForSEO - •
prospect- Lead enrichment - •
google-mcp- Google Workspace
Examples
Bad: Using MCP for simple Perplexity search
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❌ mcp__perplexity__search("latest AI news")
→ May use sonar-deep-research ($0.40+)
Good: Using research skill
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✓ Use research skill with sonar model → Uses sonar ($0.006)
Good: Using MCP for Apify web scraping
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✓ mcp__apify__call-actor for complex scraping → Many actors, MCP handles discovery
Adding New Integrations
- •Simple service, one endpoint? → Create a skill
- •Complex service, many endpoints? → Add MCP server to mcporter.json
- •Cost-sensitive? → Always create a skill with explicit cost controls