MCP vs Skills

MCP vs Skills (Based on LlamaIndex Blog)

What MCP Is

MCP (Model Context Protocol) is a standardized protocol that lets LLM agents access external tools, resources (file-like context), and parametrized prompts from third‑party services. It is mainly intended for developers familiar with CLI, authorization, schemas, and transports like stdio. Once configured, MCP tools provide precise, predictable, narrowly scoped functionality. [llamaindex.ai]

Strengths of MCP

• Very predictable output: same input → same output.
• Good for narrow, well-defined actions (e.g., scraping a webpage).
• Delivers exactly the structured info an LLM needs.
• Effective for real-world tool execution. [llamaindex.ai]

Weaknesses of MCP

• Hard to scale: many tools → discovery and management become difficult.
• Unoptimized tools can blow up context with large outputs.
• Latency: remote calls introduce network delays. [llamaindex.ai]

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What Skills Are

Skills (in the LlamaIndex / agent ecosystem) are packaged workflows, domain knowledge, or procedural instructions that guide the agent how to think or act in a given domain (additional detail supported by broader ecosystem descriptions). They define higher-level logic, context, and sequences of actions—not just a single tool call.
(Additional clarification drawn from general ecosystem information, e.g., Vercel & Skills documentation.) [vercel.com]

Strengths of Skills

• Encapsulate domain expertise, workflows, and guidelines.
• Add contextual decision-making, guiding the agent when and how to use tools.
• Great for multi-step, context-heavy, or domain-specific workflows. [vercel.com]

Weaknesses of Skills

• Less precise for direct external actions
• Not designed to integrate with external systems on their own
• They depend on the agent’s existing reasoning + tool choices

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🆚 Direct Comparison: MCP vs Skills

Aspect	MCP	Skills
Purpose	Provide standardized tool access (APIs, services, resources, prompts)	Provide knowledge + workflow logic for tasks
Target Users	Developers (need familiarity with schemas, CLI, auth)	Broader users; designed for intuitive task guidance
Granularity	Low-level, precise actions (e.g., “scrape page,” “query DB”)	High-level reasoning (e.g., “how to conduct a workflow”)
Execution Pattern	Deterministic tool call → predictable output	Procedural instructions → agent interprets them
Scaling	Harder: many tools with unique schemas, discovery issues	Easier: many skills can exist without schema overhead
Context Load	Tools may return large outputs (context bloat risk)	Skills load context progressively (usually lightweight)
Latency	Network-dependent, can be slow	Fast (local/static instructions)
Best For	Real-world actions, integrations, external data access	Workflow automation, guidelines, domain knowledge

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🎯 When to Use What

Use MCP when you need:

• Access to external systems (APIs, DBs, services)
• Precise, deterministic actions
• Structured tool interfaces
• Production‑level tool execution [llamaindex.ai]

Use Skills when you need:

• Multi-step domain workflows
• Higher-level procedural logic
• Decision-making guidance (how and when tools should be used)
• Rich context without overloading prompts [vercel.com]