> ## Documentation Index
> Fetch the complete documentation index at: https://docs.leanmcp.com/llms.txt
> Use this file to discover all available pages before exploring further.
# MCP vs Claude Skills & Code Mode
> Understanding when to use Model Context Protocol vs Claude's built-in code execution
Anthropic introduced the **Model Context Protocol (MCP)** in November 2024 as an open standard for connecting AI assistants to external tools and data sources. More recently, Anthropic also released **Claude Code Mode** — a sandboxed code execution environment that allows Claude to write and execute code directly.
Both approaches have their place. This guide breaks down the differences based on reliability, token usage, user experience, and practical use cases, drawing from empirical data and internal benchmarks.
***
## What is MCP?
MCP's primary purpose is to connect tools with LLMs securely. It enables tool builders to expose their capabilities without revealing internal implementation details.
Though MCP started as an internal Anthropic project for Claude, it quickly gained widespread adoption across the AI ecosystem. Today, MCP is supported by:
* **Workflow builders** like n8n and Gumloop
* **OpenAI** (adopted MCP for their platform)
* **IDE integrations** like Cursor and Windsurf
* **Enterprise platforms** building AI-powered applications
At its core, MCP started as a **schema + API wrapper**, but it has evolved into something much more powerful — supporting authentication, elicitation (user input forms), UI components, and more.
## What is Claude Code Mode?
Claude Code Mode is Anthropic's approach to executing tool calls **inside a sandbox** rather than through an external MCP server. Instead of calling a predefined API, the agent generates code on-the-fly and executes it within an isolated environment.
***
## Key Differences
### 1. Reliability
**More Reliable**
You define the schema, the exact API, and exactly how it works. The LLM follows your specification precisely.
**Variable Reliability**
The agent generates tool calls as code inside the sandbox. Results depend on what Claude has been trained on.
**MCP is more reliable** because you control the contract. You define the schema, arguments, and behavior. The LLM simply calls your API as specified.
**Claude Code Mode** generates code dynamically. This works well for tasks Claude has seen during training, but can fail miserably for new platforms, APIs, or custom tools that Claude hasn't learned.
If MCP was released in November 2024, Claude only got good at generating MCP-compatible code around late 2025 with Claude Opus 4.5. Until then, Claude-generated MCPs were unreliable unless you provided extensive documentation. If you don't want to wait a year for model updates, use MCP.
***
### 2. Token Usage
One of MCP's biggest challenges has been **token usage**. Every tool call includes descriptions, arguments, and schemas — which consume tokens.
This problem is largely **self-inflicted**:
> Most MCPs were auto-generated from OpenAPI specs, which contain verbose documentation meant for humans — paragraphs of explanations, detailed parameter descriptions, etc.
**Agents don't need this.** They have knowledge baked into their weights. You just need to provide hints about what the tool does. The schema itself is often sufficient.
**Best Practice:** Keep your MCP tool descriptions concise. Agents are smart — they don't need documentation-style explanations. A brief description + clear schema is enough.
Claude Code Mode avoids this overhead by generating code directly, but trades it for the reliability issues mentioned above.
***
### 3. Custom Tools & New Platforms
**New companies, custom tools, proprietary APIs**
If you're building something new, expose it as an MCP. Don't expect Claude Code Mode to magically know how to use your platform.
**Standard libraries, common tasks, calculations**
Simple math, data processing, and well-known libraries work great in Code Mode.
If you're a **startup or building new tools**, MCP is the clear choice. Claude Code Mode only works well for things it was trained on. Your custom API? It will likely fail.
***
### 4. Authentication
When your backend requires authentication, MCP provides a structured approach:
* **Token storage** handled securely on the client
* **OAuth flows** with `@leanmcp/auth`
* **Session management** built into the protocol
Claude Code Mode has no native auth handling. You'd need to pass credentials into the sandbox, which raises security concerns.
**For authenticated APIs, use MCP.**
***
### 5. UI Consistency
If you want to display UI components inside a chat interface (ChatGPT, Claude, etc.):
| Approach | Result |
| --------------- | --------------------------------------------------------------- |
| **MCP** | Consistent, predefined UI components |
| **Claude Code** | Generated HTML/JS that may look inconsistent or expose raw tags |
Users prefer consistency. They don't want to see random HTML or JavaScript tags in their chat. MCP gives you control over the presentation layer.
***
## When Claude Code Mode Shines
Claude Code Mode isn't bad — it has legitimate use cases:
### Simple Calculations
You don't need an MCP for:
* Running a calculator
* Solving a differential equation
* Summing up a list of bills
* Basic budgeting calculations
LLMs are notoriously bad at math. Running these in a sandbox makes perfect sense. Building an MCP calculator is overkill.
### Number Crunching Tasks
For quantitative finance, data analysis, or repeated execution of small functions, Claude Code Mode works well:
```python theme={null}
# Claude can generate and execute this directly
def compound_interest(principal, rate, years):
return principal * (1 + rate) ** years
```
### Well-Known Libraries
If Claude knows the library (pandas, numpy, requests to public APIs), Code Mode works fine.
***
## When MCP Wins
| Use Case | Why MCP |
| --------------------- | ----------------------------------- |
| **Custom APIs** | Claude doesn't know your API |
| **New platforms** | Not in training data |
| **Authentication** | Secure token handling |
| **Consistent UI** | No random HTML in chat |
| **Schema validation** | Structured input/output |
| **Elicitation** | Collect user input before execution |
| **Production apps** | Reliability matters |
***
## You Can Use Both
Here's the good news: **you don't have to choose**.
You can enable MCPs for your custom tools AND allow Claude to use Code Mode for simple tasks. Let Claude decide:
* Complex authenticated API call? → MCP
* Quick calculation? → Code Mode
* Custom platform integration? → MCP
* Data transformation? → Code Mode
**For SaaS developers:** Always expose your tools as MCPs. Don't rely on Claude Code Mode understanding your platform — it won't.
***
## Summary
| Factor | MCP | Claude Code Mode |
| ------------------- | ------------------------------- | --------------------------------- |
| **Reliability** | ✅ High (you control the schema) | ⚠️ Variable (depends on training) |
| **Token Usage** | ⚠️ Can be high (but fixable) | ✅ Lower |
| **Custom Tools** | ✅ Required | ❌ Will likely fail |
| **Authentication** | ✅ Built-in support | ❌ No native handling |
| **UI Consistency** | ✅ Controlled | ❌ Random HTML/JS |
| **Simple Math** | ❌ Overkill | ✅ Perfect fit |
| **Known Libraries** | ❌ Unnecessary | ✅ Works well |
**Bottom line:** Use MCP for anything custom, authenticated, or production-critical. Use Claude Code Mode for simple calculations and well-known libraries. Or use both and let Claude decide.
***
## Next Steps
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