Thoughts on the AI Agent Ecosystem

After diving deep into the AI Agent ecosystem, here are some insights worth sharing.

1. What Makes Claude Code Powerful: Linux + AI + MCP

Why is Claude Code so capable? Because it stands on the shoulders of the Linux ecosystem.

┌─────────────────────────────────────────┐
│           Claude Code (AI Layer)         │
│   Natural Language → Plan → Tools → Verify│
├─────────────────────────────────────────┤
│            MCP (Protocol Layer)          │
├─────────────────────────────────────────┤
│        Linux CLI + Web Services          │
│      grep/git/docker + GitHub/Slack      │
├─────────────────────────────────────────┤
│          Linux/Unix OS                   │
│       Pipes | Stdio | Filesystem         │
└─────────────────────────────────────────┘

Three key elements:

Linux is text-native: LLMs are fundamentally text processors; Linux’s “everything is text” philosophy is a perfect match
Unix pipe philosophy: Small composable tools; Agents can chain them together
MCP extends reach: Connects to web services that Linux CLI can’t touch

2. MCP vs Bash: An Important Clarification

I initially thought Claude Code uses MCP to call all tools, including Linux commands like grep, find, and pip.

Wrong.

Claude Code has two types of tools:

Type	Implementation	Examples
Built-in tools	Native, direct execution	Bash, Read, Write, Glob
MCP extensions	Protocol-based communication	GitHub API, Slack, databases

When Claude Code runs git status, it calls the built-in Bash tool to execute the command directly—no MCP involved.

So what is MCP for?

Scenario	Use Bash	Use MCP
Run `grep`	✅ Direct execution	❌ Unnecessary
Run Python script	✅ `python x.py`	❌ Unnecessary
Call GitHub API	❌ CLI is limited	✅ Full API access
Keep DB connection	❌ Reconnect each time	✅ Connection pooling

Core principle: MCP extends Unix philosophy, it doesn’t replace it. If native CLI can solve it, skip MCP.

Deep Dive: Claude Code’s Tool Architecture

From Claude model’s perspective, native tools and MCP tools look the same—both are called via Anthropic’s Tool Use API. The difference is in implementation.

Architecture diagram

Claude model outputs:
{
  "type": "tool_use",
  "name": "Bash",
  "input": {"command": "git status"}
}

┌─────────────────────────────────────────────────────┐
│                   Claude Model                       │
│              (outputs tool_use requests)             │
└─────────────────────┬───────────────────────────────┘
                      │ Tool Use API
                      ▼
┌─────────────────────────────────────────────────────┐
│               Claude Code (Host)                     │
│   ┌─────────────────┐    ┌─────────────────────┐   │
│   │  Native Tools   │    │    MCP Client       │   │
│   │  (TypeScript)   │    │         │           │   │
│   │  Bash → exec()  │    │         ▼           │   │
│   │  Read → fs.read │    │   MCP Protocol      │   │
│   └─────────────────┘    └─────────┬───────────┘   │
└─────────────────────────────────────┼───────────────┘
                                      ▼
                            ┌─────────────────┐
                            │   MCP Servers   │
                            └─────────────────┘

Why Not Unify Everything with MCP?

1. Performance

Native tool:  Function call → Execute → Return           ~1ms
MCP tool:     Serialize → IPC → Parse → Execute → Return  ~10-100ms

2. Reliability

Native tools: Always available, no external dependencies
MCP: Server process could crash, timeout

3. Bootstrapping

Need basic tools to install MCP servers
Can’t use MCP to install MCP

Analogy: Kernel Mode vs User Mode

┌─────────────────────────────────────────┐
│           Operating System              │
├───────────────────┬─────────────────────┤
│  User Mode        │  Kernel Mode        │
│  (applications)   │  (core functions)   │
│  syscall needed   │  direct execution   │
│  isolated         │  fast & reliable    │
└───────────────────┴─────────────────────┘

┌─────────────────────────────────────────┐
│            Claude Code                  │
├───────────────────┬─────────────────────┤
│  MCP Tools        │  Native Tools       │
│  (extensions)     │  (core functions)   │
│  protocol needed  │  direct execution   │
│  isolated         │  fast & reliable    │
└───────────────────┴─────────────────────┘

Design principle: Core functionality should be fast and reliable; extensions go through a standard interface.

3. Two Evolution Tracks

“Make machines do work for humans” has evolved along two different tracks:

Automation Track (Event-Driven)

Year	Milestone	Characteristics
1970s	Cron	Scheduled tasks, requires scripting
2010	IFTTT	”If This Then That”, accessible to everyone
2014	Shortcuts	Mobile automation
2019	n8n	Open source, self-hosted, complex workflows
2024	Dify, Coze	Workflows + AI nodes

Characteristic: Trigger-driven, runs automatically once set up, no human intervention needed.

AI Assistant Track (Human-Driven)

Year	Milestone	Characteristics
2022	ChatGPT	Conversational AI, Q&A
2024	Claude Code	Task-driven Agent, can execute actions

Characteristic: Human-initiated, you give a task, AI completes it.

Convergence of Two Tracks

The current trend is convergence:

n8n/Dify add AI nodes → Automation becomes smarter
Claude Code can be called by other tools → Agents can integrate into automation workflows

How platforms relate to MCP:

Platform	Type	MCP Support
Claude Code	AI Assistant	Native
Dify	Automation + AI	Supported (v1.6.0+)
n8n	Automation + AI	Compatible
Coze	Automation + AI	No

4. The Real Competition Among Agent Platforms

Every company is building Agent platforms, but after MCP standardization, technical barriers are actually thin.

What is everyone doing?

Layer	Work	Technical Difficulty
Model adaptation	Integrate their own models	Low
Tool wrapping	Package as MCP Tools	Low
Prompt engineering	System prompts, orchestration	Medium
New tools	Develop MCP Servers	Medium

The real moats aren’t technical:

Vendor	Real Moat
ByteDance Coze	Traffic (Feishu, Douyin)
Alibaba Bailian	Enterprise customers (DingTalk, Aliyun)
OpenAI	Model capability
Anthropic	Protocol standard (MCP)

Browser wars analogy:

MCP Protocol ≈ HTTP Protocol
Tools        ≈ Websites
Prompts      ≈ Rendering engine

After protocol standardization, competition becomes:
├── Who has more "websites" (tools)
├── Who has more users
└── Who "renders" (understands) better

Takeaways:

Don’t chase the “latest” platform—features converge
Open source (Dify/n8n) can replace commercial platforms
Learn one platform, you basically know them all

5. Local LLM: Balancing Privacy and Cost

Consideration	Cloud API	Local Deployment
Privacy	Data sent to third party	Data stays local
Cost	Pay per token	Fixed hardware investment
Latency	Network delay	Local response

Popular options:

Ollama: Quick start, consumer GPU
vLLM: Production environment, high concurrency
llama.cpp: Ultra lightweight, runs on CPU

Hands-on: Build a Fully Offline AI Agent

Local model + MCP = Fully offline AI Agent. Here’s the architecture:

┌─────────────────────────────────────────┐
│            MCPHost                       │
│  (bridges local LLM with MCP servers)   │
├──────────────────┬──────────────────────┤
│   Local LLM      │    MCP Servers       │
│   (Ollama)       │    (local tools)     │
└──────────────────┴──────────────────────┘

Run mcphost -m ollama:qwen2.5:7b --config ./mcp-config.json and you have a fully offline AI Agent.

Setup steps

Step 1: Install Ollama + model

curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5:7b  # or llama3.2, mistral, etc.

Step 2: Install MCPHost

go install github.com/mark3labs/mcphost@latest

Step 3: Create MCP config (mcp-config.json)

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user/documents"]
    },
    "sqlite": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sqlite", "~/data.db"]
    }
  }
}

Step 4: Run

mcphost -m ollama:qwen2.5:7b --config ./mcp-config.json

Now you have a fully offline AI Agent that can:

Read/write files (filesystem MCP)
Query databases (sqlite MCP)
Use any local MCP server you configure

Open Source Alternative: goose

MCPHost has a limitation: it only provides MCP capabilities, not Claude Code’s native tools (Bash, Read, Write, etc.).

goose (open sourced by Block) is the closest open source alternative to Claude Code:

Capability	Claude Code	goose	MCPHost
Native shell execution	✅	✅	❌
File read/write	✅	✅	Via MCP
MCP support	✅	✅	✅
Local models	❌	✅	✅
Open source	❌	✅	✅

Install and use goose:

# Install
pipx install goose-ai
 
# Run with local model
goose session --provider ollama --model qwen2.5:7b

Bottom line: Want open source + local models + Claude Code-like experience → use goose

Key Takeaways

Claude Code is powerful because of Linux: Text-native + pipe philosophy + MCP extension
MCP doesn’t replace Bash: Use Bash for native CLI tasks, MCP for web services
Rules vs Intent: Different abstraction levels, each has its place
Agent platform technical barriers are thin: Competition is about ecosystem, users, and models
Local deployment is mature: A viable option for privacy-sensitive scenarios

January 2026

Y.C.Lab3

Explorer

Thoughts on AI Agent Ecosystem