MCP vs Skills: The Hard Truth About Scaling AI Workflows

I spent three days last month tearing apart an AI agent workflow that just wouldn't scale. We had hardcoded a dozen custom "skills"—functions for pulling GitHub issues, querying Jira, and checking Datadog. Every time an API changed, the agent broke. I was spending more time fixing the agent than the agent was saving me.

If you are building AI agents right now, you know this pain. The big debate today isn't which LLM to use, but how to connect your LLM to your infrastructure: Do you standardize connectivity using the new Model Context Protocol (MCP), or do you stick to defining custom agent Skills?

Written from a developer perspective working with AI systems and backend architectures, here is the harsh reality: MCP alone is not useful in most real-world scenarios, and Skills without MCP are limited but predictable. Here is exactly how they differ, and how to combine them.

The Core Difference: Hands vs. Brain

When developers first hear about MCP, they often confuse it with a new way to write skills. It isn't.

Think of it like this:

• MCP is the access layer (the hands): It standardizes how an LLM reaches into a system (like Slack, Postgres, or GitHub). It does not know what to do with that data; it just provides a standardized universal cable to fetch it.
• Skills are the logic/workflow layer (the brain): A skill is a defined behavior. It tells the agent when to search Slack, how to summarize the thread, and where to post the resolution.

If you just give an LLM an MCP server connected to your database, it has hands but no instructions. If you give an LLM a custom skill without a standardized protocol, you have to write a bespoke integration for every single tool.

Experience: Running MCP vs Skills in a Real Setup

In my setup, I decided to test a hybrid approach using an automated API triage agent.

What I built/tested: An agent designed to monitor a #production-alerts Slack channel, read the logs, and draft an incident report in Notion.

What I noticed was: Building this using purely hardcoded Skills took roughly 400 lines of brittle integration code. I had to manage the Slack API auth, the Notion API auth, and the data parsing natively in my codebase.

When I refactored to use Anthropic's provided MCP servers for Slack and Notion, the integration code dropped to nearly zero. I just connected the client. However, the agent suddenly lacked direction. It could read Slack, but it didn't know which messages mattered.

The limitation: MCP gave my agent universal access, but I still had to write the "Skills" (the system prompts and defined tool-use schemas) to give it a reliable workflow.

Comparison: MCP vs Skills

| Feature | Model Context Protocol (MCP) | Agent Skills | | :--- | :--- | :--- | | Purpose | Standardized connection to data sources and tools. | Specific workflows, behaviors, and business logic. | | Complexity | High setup (requires running servers), zero ongoing integration. | High maintenance (bespoke API coding for every new tool). | | Control | Low. The LLM decides what to do with the exposed resources. | High. You define the exact steps the agent must take. | | Real-world usage | Database querying, filesystem access, universal integration. | Multi-step reasoning, data transformation, scheduled tasks. | | Dependency | Needs an MCP-compatible client (like Claude Desktop). | Works with any basic LLM implementation. |

Real-World Use Cases

So, when do you use which? Here are three scenarios I've encountered:

1. Debugging and Log Analysis System

• When MCP is required: You need the agent to search through massive, live logs across Datadog, AWS CloudWatch, and local files. Instead of writing custom APIs for all three, you attach an MCP server for each. The LLM gets universal read access.
• When Skills are required: The agent needs the skill to "Identify memory leaks." This is logic. It tells the model to look for OOM errors and correlate them with deployment timestamps.
• How they work together: The Skill defines the troubleshooting methodology; the MCP server executes the secure, standardized data retrieval.

2. API Automation Agent

• When MCP is required: You are building an agent that needs to introspect your internal GraphQL API or interact with a constantly changing OpenAPI spec.
• When Skills are required: You need a specific action, like "Refund User Transaction," that is heavily gated by business rules (i.e., checking a user's subscription tier before refunding).
• How they work together: MCP reads the API documentation natively. The Skill enforces the business logic prior to executing the final API call.

3. CI/CD or Workflow Automation

• When MCP is required: When your agent needs to read the entire GitHub repository context to understand a PR.
• When Skills are required: When the agent needs to automatically label the PR, run a test suite, and post a specific format of code review.
• How they work together: You cannot hardcode a GitHub ingestion pipeline efficiently—use MCP. But you absolutely must hardcode the PR review standards—use a Skill.

Conclusion

The industry is rapidly shifting toward standardized protocols, and for good reason. My biggest takeaway from migrating production agents to MCP is this: Stop writing integration code.

Let MCP handle the messy realities of API communication, authentication, and data retrieval. Focus 100% of your engineering time on writing deterministic, highly reliable Skills. Combining a universal access layer with strongly typed behavioral skills is the only way to build AI agents that actually survive contact with production.