Don't know how to make AI read local databases and the latest documentation? This Claude Code tutorial takes you deep into the Model Context Protocol (MCP) and custom skills via
SKILL.md. Step-by-step, we'll teach you how to configuremcp.json, integrate GitHub, Playwright, and Context7, and build a zero-hallucination automated Code Review workflow.
In the previous article, we introduced the basic environment setup and context management for Claude Code. Running Claude Code requires a Node.js environment, and with ServBay, we can deploy the local environment with one click and zero configuration. However, knowing the basics isn't enough; developers will inevitably encounter new technical bottlenecks during use.
If your business needs to integrate a newly released third-party API, but the program's training data hasn't been updated, what happens when the AI writes code based on outdated information? Usually, it will force code generation based on older logic. So, how do we solve AI hallucinations? Furthermore, a frontend engineer might want the machine to check if page styles are misaligned, or a backend engineer might need to verify database fields. Simple local code read access can no longer meet these demands.
This advanced Claude Code tutorial will focus on unpacking two high-level features: Claude Skills for building internal workflows, and the Claude Code MCP protocol for opening external data channels. Mastering these two technologies can transform a standalone code assistant into a full-stack R&D collaborator.
Prerequisites for Advanced Operations
Before diving into the configuration, please ensure your local development environment meets the following conditions:
Mastered Basic Configuration: It is recommended to read the first part of this series first to ensure you have completed the basic environment initialization and are familiar with how to manage conversational context.
Configure Node.js 18+ Environment: Running various MCP servers requires a relatively new Node environment. We recommend using ServBay, a local web development environment management tool. Through its intuitive dashboard, you can install and switch Node.js 18+ versions with just one click, saving you the tedious steps of manually configuring system environment variables.
Prepare a Project with a UI: Have a local project containing frontend pages ready to later experience the visual testing capabilities of the Playwright plugin.
Obtain GitHub Account Permissions: Prepare a GitHub account and a Personal Access Token with repository access (used to demonstrate automated GitHub MCP workflows).
Establishing Standards and Claude Skills
Relying solely on manually typing lengthy prompts every time to make the program automatically complete specific tasks is highly inefficient. Claude Skills provides a mechanism to define standardized operational workflows.
Skill files are essentially Markdown specifications stored in specific directories. When a developer makes a request in natural language, the program automatically matches and triggers the corresponding skill, thereby executing the task according to preset, professional steps.
Developers can save project-specific skills in the .claude/skills/ folder at the project root, or place universally applicable skills in the system-level ~/.claude/skills/ directory.
How to Write SKILL.md
The foundation of creating a practical skill is writing a clear configuration file. Here, we use an automated security review skill as an example to demonstrate the basic structure of the configuration file.
---
name: Security and Compliance Review
description: "Conduct a comprehensive security vulnerability scan and format validation on submitted code"
triggers:
- Review changed code
- Execute security scan
- Check code compliance
allowed-tools:
- Read
- Glob
- Bash(git diff HEAD)
---
# Review Execution Guidelines
## Step-by-Step Breakdown
1. Run `git diff HEAD` to fetch current uncommitted code differences
2. Filter out changed files and categorize them by language
3. Perform a line-by-line comparison based on the security standards below
4. Compile clear review conclusions
## Mandatory Security Checks
- Ensure no database connection strings or keys are hardcoded in the files
- Verify that all external input parameters have undergone type validation
- Check if all asynchronous requests include exception handling mechanisms
## Output Formatting Requirements
- 🔴 Blocking Risk [Point out the specific location and provide fix code]
- 🟡 Potential Hazard [Explain the potential issues it might cause]
- 🟢 Good Practice [Note well-written code]
The top of this configuration uses YAML format to define basic properties. triggers defines the keywords that awaken this skill. allowed-tools sets the security boundaries, restricting the skill to only reading files and executing a specific range of Git commands, preventing accidental modification or deletion of files during the review process.
To save conversational memory, complex skill instructions shouldn't be piled into a single file. You can take a modular approach, using @reference.md in the main file to reference external detailed rulebooks, achieving on-demand loading.
A Bridge to the External World
With internal execution standards in place, the next step is to solve the problem of acquiring external data. This leads to a new technology currently receiving a lot of attention in the developer community.
What is the MCP Protocol?
The Model Context Protocol (MCP) is an open communication standard. Its purpose is to provide a universal set of interfaces for AI models, enabling them to safely connect to external tools and data sources. If Skills are the methodology guiding the work, then Claude Code MCP is the actual toolbox needed to execute that work.
By running small MCP server programs locally, developers can expose capabilities like web scraping, database querying, and version control for large language models to call.
mcp.json Configuration Tutorial
All external server connections need to be registered in the .claude/mcp.json file. Below is a configuration template containing common services, showing how to configure environmental parameters and startup commands.
{
"mcpServers": {
"github_connect": {
"command": "npx",
"args": ["-y", "@anthropic-ai/mcp-server-github"],
"env": {
"GITHUB_TOKEN": "${GITHUB_TOKEN}"
}
},
"doc_fetcher": {
"command": "npx",
"args": ["-y", "@anthropic-ai/mcp-server-context7"]
},
"ui_tester": {
"command": "npx",
"args": ["-y", "@anthropic-ai/mcp-server-playwright"]
}
}
}
Place this file in your project-level or global configuration directory, and the program will automatically mount these capabilities upon startup. If mounting fails, you can append the --mcp-debug parameter in the terminal to view specific error logs.
Four High-Frequency Real-World Scenarios
Once configured, your development experience will be significantly enhanced. Here are several typical application scenarios that solve actual pain points.
Fetching the Latest Documentation to Solve Information Lag
In the face of frequent frontend framework updates, using the Context7 MCP can perfectly avoid interference from outdated data. When a developer asks to write a component using the latest React features, the program will automatically call this service to scrape the official real-time documentation and output code according to the newest API specs, fundamentally eliminating technical hallucinations.
Introducing Visual Feedback to Complete the Frontend Loop
Often, the generated UI code logic is correct, but the styling is slightly off. With the Playwright plugin, AI testing of frontend pages becomes highly intuitive. The program can launch a headless browser in the background, access the local development server, and perform screenshot analysis on the rendered page. It can detect issues like obscured buttons or inconsistent margins just like a human engineer, and modify the CSS code accordingly.
Gaining Insight into Underlying Storage Structures
When writing complex business logic, allowing the AI to read the local database structure is key to improving accuracy. After configuring connection plugins for PostgreSQL or SQLite, the program can directly query actual table structures, field types, and relationship constraints. Then, when you ask it to write data migration scripts or JOIN query statements, it can perfectly match your current business data model.
Seamless Integration with Code Hosting Platforms
After configuring a GitHub access token, the program can pull remote pull request details directly within the terminal. If it finds issues, it can even call the API directly to create an Issue or add review comments on the code hosting platform, all without needing to open a browser.
The Synergy of Skills and Peripherals
Combining standards with data sources unleashes extremely powerful automation capabilities.
Imagine a daily development workflow: A developer enters a command in the terminal, requesting to verify the latest commit and confirm that the frontend display is correct.
Upon receiving the command, the GitHub plugin is responsible for pulling the code diffs, the code review skill provides the evaluation criteria, Context7 verifies whether third-party libraries are used correctly, and finally, Playwright accesses the preview URL for screenshot validation. Once everything is verified without errors, the review report is automatically synced to the remote repository.
Even more interestingly, developers can ask the program to write skill specifications itself. Save a brand-new third-party payment API document locally, and command the program to read the document and generate an integration skill for the current project. It will automatically extract authentication methods and error handling rules, generating a complete SKILL.md to store in the skills library for future reuse.
Once developers are proficient in configuring these advanced modules, a highly efficient, accurate, and business-aware R&D environment is successfully built. In the upcoming series of tutorials, we will explore more macroscopic architectural practices, discussing how to utilize multi-instance parallel development and automated pipeline integration to handle large-scale engineering projects.
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