smithery/arpowers

mcp-guide

Guidance for when to use MCP vs direct API calls

Installation

$ npx skills add smithery/arpowers --skill mcp-guide

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More details

Agent compatibility

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Skill metadata

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Version1.0.0

Package contents

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  • skill md SKILL.md 3,782 B
  • docs SUMMARY.md 65 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

MCP vs Skills/Direct API Guide

Decision framework for choosing between MCP tools and direct API calls.

Quick Decision

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 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 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

❌ mcp__perplexity__search("latest AI news")
   → May use sonar-deep-research ($0.40+)

Good: Using research skill

✓ Use research skill with sonar model
   → Uses sonar ($0.006)

Good: Using MCP for Apify web scraping

✓ mcp__apify__call-actor for complex scraping
   → Many actors, MCP handles discovery

Adding New Integrations

  1. Simple service, one endpoint? → Create a skill
  2. Complex service, many endpoints? → Add MCP server to mcporter.json
  3. Cost-sensitive? → Always create a skill with explicit cost controls