MCP gives your agent access to external systems while Agent Skills teach it your team's patterns and conventions. The problem is that developers keep treating AI Agent Skills and MCP as competing standards when they actually work at different layers of your architecture. You'll save yourself hours of debugging once you understand that one handles connections while the other handles knowledge, and your agent needs both to write code that works on the first try.
TLDR:
MCP connects agents to live data sources; Agent Skills teach agents your implementation patterns
Skills use progressive disclosure to avoid context bloat while MCP loads all tools upfront
MCP runs 3-4x faster at runtime but Skills preserve tokens until needed
You can install Velt Agent Skills via
npx skills add velt-js/agent-skillsfor zero-docs integration
What Is MCP (Model Context Protocol)
MCP is an open protocol that Anthropic introduced in November 2024 to solve a fundamental problem: every AI app was building custom integrations to connect with external tools and data sources. Instead of each developer writing unique code to let Claude or ChatGPT access a database, API, or file system, MCP standardizes those connections into a single format. At its core, MCP uses a client-server architecture. Your AI app (the client) connects to an MCP server, which exposes resources like databases, APIs, or file systems. When your LLM needs information from Notion, GitHub, or Google Drive, the MCP server handles the request and returns the data in a format the model can understand.
What Are Agent Skills
Agent Skills are structured knowledge packages that teach AI coding agents how to implement specific tools or frameworks correctly. Each Skill lives in a SKILL.md file inside a folder that agents can find and reference. The file contains implementation rules, code examples, and common pitfalls. When a developer installs a Skill, their AI agent gains access to that knowledge and can reference it when generating code. Anthropic originally developed the Agent Skills format and released it as an open standard. Since then, coding agents like Claude Code, Cursor, and others have adopted it to give agents a reliable source of truth.
The Core Architectural Difference: Connection vs Knowledge
MCP works at the connection layer as a runtime protocol that links AI agents to external systems like databases, APIs, and files. When your agent needs live data or resources, MCP handles that exchange during execution. Agent Skills, on the other hand, function at the knowledge layer. They provide rules, patterns, and examples that shape how agents write code before any connections occur. Skills don't fetch data or execute operations. They guide the agent's code generation with your team's conventions and best practices.
MCP answers whether an agent can access a resource. Skills answer whether an agent knows how to implement something correctly. An MCP server might connect Claude to your Postgres database, but won't teach Claude your schema patterns. Skills fill that gap.
This makes them complementary instead of competing, similar to how collaboration SDK architectures use different layers. MCP standardizes runtime integrations. Skills encode implementation knowledge that reduces bugs and iteration cycles. Your agent uses both: Skills to write correct integration code, MCP to execute those connections.
Progressive Disclosure: The Key to Agent Skills
Progressive disclosure is the design principle that keeps Agent Skills from overwhelming your agent's context window. Instead of loading every rule upfront, Skills reveal information gradually based on what the agent needs. Each Skill starts with minimal metadata: a short description and priority level. When you prompt your agent to build a feature, it scans these lightweight summaries to determine relevance. If the Skill matches your request, the agent loads the full instruction set. If not, it stays out of the context window. This matters because context windows have limits. Loading four complete Skills with hundreds of rules would exhaust tokens before the agent writes code. Progressive disclosure treats Skills as just-in-time references instead of permanent memory, pulling detailed instructions only when generating implementation code.
MCP Tools: Real-Time Execution and External Capabilities
MCP servers expose three types of capabilities that extend what LLMs can do beyond text generation:
Resources let agents read data from external sources like databases or file systems.
Tools let agents execute functions that change state or perform computations.
Prompts provide reusable templates that guide how agents interact with those resources and tools.
Tools are where MCP becomes actionable. When your LLM generates a response that requires creating a Jira ticket, querying a SQL database, or updating a spreadsheet, an MCP tool handles that execution. The agent calls the tool, the MCP server processes the request against the connected system, and returns the result to continue the conversation.
This real-time execution separates MCP from static knowledge sources. Agent Skills might teach Claude how to structure a database query, but an MCP tool actually runs that query and retrieves the data.
When to Use MCP
MCP works best when your agent needs real-time access to external systems. Building agents that create GitHub issues, query production databases, or send Slack messages requires connections beyond static training data. MCP standardizes these interactions instead of writing custom API wrappers for each service. At its core, MCP handles authenticated operations like reading calendars, updating CRM records, or triggering deployments, which matters for enterprise-ready collaboration scenarios. The server manages credentials and permissions while keeping API keys out of agent code, just as you would when building Google Docs style comments.
So, you should use MCP when multiple agents share resources. One MCP server lets Claude, Copilot, and Cursor access the same data without duplicate integrations or scattered authentication. And, you should skip MCP for static documentation or coding patterns. It's built for fresh data retrieval and stateful operations, not teaching agents from fixed content.
When to Use Agent Skills
Agent Skills work best when your team needs to standardize how agents implement specific tools or frameworks. If developers keep asking Claude to add the same library but get different patterns each time, a Skill codifies the correct approach once. Skills shine for fast-evolving SDKs where documentation lags behind releases, like those implementing collaborative editor features. A single skills.md update fixes every agent's knowledge instantly. Non-developers can create Skills without engineering infrastructure. Writing a markdown file with examples and rules requires no server setup, authentication, or API management. Product managers who document workflows can translate that documentation into Skills that agents execute.
So, you should use Skills to capture team conventions that LLMs can't infer from public training data. Your database naming standards, API error handling patterns, or component architecture decisions aren't in Claude's memory. Skills encode that institutional knowledge so new team members and agents follow the same rules. And, you should skip Skills for one-off implementations or when public documentation already covers your use case.
Runtime Performance: MCP vs Skills
It's important to understand how using either of these AI tools, MCP or Skills, impacts performance at runtime. If the impact is too great, the agent's ability to complete the required tasks as quickly as possible will be compromised, undermining the user experience. So how do these two elements of AI operations affect performance?
MCP loads all tools and resources when your agent starts a session. This upfront work means faster execution during conversation because the agent already knows what's available. When you ask Claude to create a calendar event, it calls the MCP tool directly without scanning or loading extra context.
Skills load incrementally through progressive disclosure. The agent scans Skill metadata, decides relevance, then loads full instructions only when generating code. This multi-step process adds overhead that MCP's preloaded approach avoids. In practice, MCP makes roughly 3 to 4 times fewer tool invocations than progressive disclosure during runtime.
The tradeoff is context usage versus execution speed. MCP sacrifices tokens upfront to gain speed later. Skills preserve tokens but pay a small latency cost when loading instructions mid-task.
How MCP and Agent Skills Work Together
As we called out earlier, these two approaches aren't opposing and they aren't mutually exclusive. They work together. For example, MCP handles the connection to your Postgres database while Agent Skills teach the agent your naming conventions, migration patterns, and query optimization rules. Together, your agent can both access the database and write queries that match your team's standards.
In a typical setup, you might run an MCP server that connects to Slack, GitHub, and your analytics database. Your Skills library includes implementation guides for your internal design system, testing framework, and deployment workflow. When a developer asks their agent to build a feature that posts deployment notifications to Slack, the agent uses Skills to generate code following your patterns (like choosing a commenting SDK), then uses MCP to execute the actual Slack API call.
MCP (Model Context Protocol) | Agent Skills | |
|---|---|---|
Primary Purpose | Connect agents to external systems and data sources | Teach agents implementation patterns and conventions |
Architecture Layer | Connection/Runtime layer | Knowledge layer |
Loading Strategy | All tools loaded upfront at session start | Progressive disclosure - loads only when needed |
Runtime Performance | 3-4x faster execution (fewer tool invocations) | Slight latency from incremental loading |
Context Usage | Higher upfront token cost | Preserves tokens until needed |
Setup Complexity | Requires server infrastructure and authentication | Simple markdown file with YAML frontmatter |
Best For | Real-time data access, authenticated operations, shared resources | Team conventions, SDK patterns, institutional knowledge |
Update Process | Requires server/integration changes | Edit markdown file - takes effect immediately |
Non-Developer Friendly | No - requires technical setup | Yes - markdown documentation skills sufficient |
Creating Your First Agent Skill
Agent Skills use a simple file structure. Create a folder with your skill name, add a SKILL.md file inside, and start with YAML frontmatter that defines metadata. The frontmatter includes a short description (under 100 characters) and a priority level that tells agents when to load your skill.
Your skill description should explain what problem it solves instead of listing features. For example, describing how to implement Google Docs style commenting prevents common DOM errors. "Teaches Velt comment implementation patterns to prevent DOM placement errors" works better than "Velt integration guide." Include both correct and incorrect code examples so agents learn what to avoid, similar to how CRDT libraries document conflict resolution patterns. Installing skills across coding agents is handled by Vercel's CLI. Running npx skills add pulls your skill into 20+ agents including Claude Code, Cursor, and OpenCode.
Building Collaboration into Your AI Tools with Velt
When you're building AI-powered apps that need collaboration features, you face the same architectural decisions: should you provide runtime capabilities, implementation knowledge, or both?
Velt solves this with a collaboration SDK paired with Agent Skills. While Velt's SDK handles the runtime layer (comments, presence, notifications, recordings), the Velt Agent Skills teach coding agents how to implement these features correctly. Install with npx skills add velt-js/agent-skills and your AI agent gains access to four core skills covering setup, comments, CRDT editing, and notifications.
Each skill includes priority-ranked rules with correct and incorrect patterns. You get first-try accuracy without reading docs or debugging common mistakes like incorrect DOM bindings or missing document initialization, which is why many teams consider the build vs buy decision for comments.
Final Thoughts on MCP vs Agent Skills
The difference between skills and MCP comes down to knowledge versus access. Agent Skills teach your coding agents how to implement features correctly, while MCP gives them real-time connections to databases, APIs, and external tools. You can use one without the other, but combining them gives your agents both the knowledge and capabilities they need. Book a demo to see how Velt's collaboration SDK works with Agent Skills to simplify implementation.
FAQ
How does MCP handle authentication for external services?
MCP servers manage credentials and permissions centrally, keeping API keys and tokens out of your agent code while handling authenticated operations like reading calendars or updating CRM records.
Can I use Agent Skills and MCP together in the same project?
Yes, they're designed to work together. MCP handles runtime connections to external systems while Agent Skills teach your coding agent the correct implementation patterns for your team's conventions and frameworks.
What happens if I update a Skill file after installing it?
Your agent will reference the updated version immediately on the next task. This makes Skills ideal for fast-evolving SDKs where a single markdown update fixes every agent's knowledge without redeploying infrastructure.
Why does MCP make fewer tool invocations than Skills during runtime?
MCP loads all tools and resources when your session starts, so the agent knows what's available upfront. Skills use progressive disclosure, loading instructions only when needed, which adds a small overhead but preserves context window space.
Do I need coding experience to create Agent Skills?
No. Skills are markdown files with YAML frontmatter and code examples. Product managers or technical writers who document workflows can create Skills without managing servers, APIs, or authentication systems.
