🚀 Build and share your MCP servers with the world. Once you’ve created a great MCP server, submit it to the Cline MCP Marketplace to make it discoverable and one-click installable by thousands of developers.
What Are MCP Servers?
Model Context Protocol (MCP) servers extend AI assistants like Cline by giving them the ability to:
- Access external APIs and services
- Retrieve real-time data
- Control applications and local systems
- Perform actions beyond what text prompts alone can achieve
Without MCP, AI assistants are powerful but isolated. With MCP, they gain the ability to interact with virtually any digital system.
The Development Protocol
The heart of effective MCP server development is following a structured protocol. This protocol is implemented through a .clinerules
file that lives at the root of your MCP working directory (/Users/your-name/Documents/Cline/MCP).
Using .clinerules
Files
A .clinerules
file is a special configuration that Cline reads automatically when working in the directory where it’s placed. These files:
- Configure Cline’s behavior and enforce best practices
- Switch Cline into a specialized MCP development mode
- Provide a step-by-step protocol for building servers
- Implement safety measures like preventing premature completion
- Guide you through planning, implementation, and testing phases
Here’s the complete MCP Server Development Protocol that should be placed in your .clinerules
file:
# MCP Server Development Protocol
⚠️ CRITICAL: DO NOT USE attempt_completion BEFORE TESTING ⚠️
## Step 1: Planning (PLAN MODE)
- What problem does this tool solve?
- What API/service will it use?
- What are the authentication requirements?
□ Standard API key
□ OAuth (requires separate setup script)
□ Other credentials
## Step 2: Implementation (ACT MODE)
1. Bootstrap
- For web services, JavaScript integration, or Node.js environments:
```bash
npx @modelcontextprotocol/create-server my-server
cd my-server
npm install
```
- For data science, ML workflows, or Python environments:
```bash
pip install mcp
# Or with uv (recommended)
uv add "mcp[cli]"
```
2. Core Implementation
- Use MCP SDK
- Implement comprehensive logging
- TypeScript (for web/JS projects):
```typescript
console.error("[Setup] Initializing server...")
console.error("[API] Request to endpoint:", endpoint)
console.error("[Error] Failed with:", error)
```
- Python (for data science/ML projects):
```python
import logging
logging.error('[Setup] Initializing server...')
logging.error(f'[API] Request to endpoint: {endpoint}')
logging.error(f'[Error] Failed with: {str(error)}')
```
- Add type definitions
- Handle errors with context
- Implement rate limiting if needed
3. Configuration
- Get credentials from user if needed
- Add to MCP settings:
- For TypeScript projects:
```json
{
"mcpServers": {
"my-server": {
"command": "node",
"args": ["path/to/build/index.js"],
"env": {
"API_KEY": "key"
},
"disabled": false,
"autoApprove": []
}
}
}
```
- For Python projects:
```bash
# Directly with command line
mcp install server.py -v API_KEY=key
# Or in settings.json
{
"mcpServers": {
"my-server": {
"command": "python",
"args": ["server.py"],
"env": {
"API_KEY": "key"
},
"disabled": false,
"autoApprove": []
}
}
}
```
## Step 3: Testing (BLOCKER ⛔️)
<thinking>
BEFORE using attempt_completion, I MUST verify:
□ Have I tested EVERY tool?
□ Have I confirmed success from the user for each test?
□ Have I documented the test results?
If ANY answer is "no", I MUST NOT use attempt_completion.
</thinking>
1. Test Each Tool (REQUIRED)
□ Test each tool with valid inputs
□ Verify output format is correct
⚠️ DO NOT PROCEED UNTIL ALL TOOLS TESTED
## Step 4: Completion
❗ STOP AND VERIFY:
□ Every tool has been tested with valid inputs
□ Output format is correct for each tool
Only after ALL tools have been tested can attempt_completion be used.
## Key Requirements
- ✓ Must use MCP SDK
- ✓ Must have comprehensive logging
- ✓ Must test each tool individually
- ✓ Must handle errors gracefully
- ⛔️ NEVER skip testing before completion
When this .clinerules
file is present in your working directory, Cline will:
- Start in PLAN MODE to design your server before implementation
- Enforce proper implementation patterns in ACT MODE
- Require testing of all tools before allowing completion
- Guide you through the entire development lifecycle
Getting Started
Creating an MCP server requires just a few simple steps to get started:
1. Create a .clinerules
file (🚨 IMPORTANT)
First, add a .clinerules
file to the root of your MCP working directory using the protocol above. This file configures Cline to use the MCP development protocol when working in this folder.
2. Start a Chat with a Clear Description
Begin your Cline chat by clearly describing what you want to build. Be specific about:
- The purpose of your MCP server
- Which API or service you want to integrate with
- Any specific tools or features you need
For example:
I want to build an MCP server for the AlphaAdvantage financial API.
It should allow me to get real-time stock data, perform technical
analysis, and retrieve company financial information.
3. Work Through the Protocol
Cline will automatically start in PLAN MODE, guiding you through the planning process:
- Discussing the problem scope
- Reviewing API documentation
- Planning authentication methods
- Designing tool interfaces
When ready, switch to ACT MODE using the toggle at the bottom of the chat to begin implementation.
4. Provide API Documentation Early
One of the most effective ways to help Cline build your MCP server is to share official API documentation right at the start:
Here's the API documentation for the service:
[Paste API documentation here]
Providing comprehensive API details (endpoints, authentication, data structures) significantly improves Cline’s ability to implement an effective MCP server.
Understanding the Two Modes
PLAN MODE
In this collaborative phase, you work with Cline to design your MCP server:
- Define the problem scope
- Choose appropriate APIs
- Plan authentication methods
- Design the tool interfaces
- Determine data formats
ACT MODE
Once planning is complete, Cline helps implement the server:
- Set up the project structure
- Write the implementation code
- Configure settings
- Test each component thoroughly
- Finalize documentation
Case Study: AlphaAdvantage Stock Analysis Server
Let’s walk through the development process of our AlphaAdvantage MCP server, which provides stock data analysis and reporting capabilities.
Planning Phase
During the planning phase, we:
- Defined the problem: Users need access to financial data, stock analysis, and market insights directly through their AI assistant
- Selected the API: AlphaAdvantage API for financial market data
- Standard API key authentication
- Rate limits of 5 requests per minute (free tier)
- Various endpoints for different financial data types
- Designed the tools needed:
- Stock overview information (current price, company details)
- Technical analysis with indicators (RSI, MACD, etc.)
- Fundamental analysis (financial statements, ratios)
- Earnings report data
- News and sentiment analysis
- Planned data formatting:
- Clean, well-formatted markdown output
- Tables for structured data
- Visual indicators (↑/↓) for trends
- Proper formatting of financial numbers
Implementation
We began by bootstrapping the project:
npx @modelcontextprotocol/create-server alphaadvantage-mcp
cd alphaadvantage-mcp
npm install axios node-cache
Next, we structured our project with:
src/
├── api/
│ └── alphaAdvantageClient.ts # API client with rate limiting & caching
├── formatters/
│ └── markdownFormatter.ts # Output formatters for clean markdown
└── index.ts # Main MCP server implementation
API Client Implementation
The API client implementation included:
- Rate limiting: Enforcing the 5 requests per minute limit
- Caching: Reducing API calls with strategic caching
- Error handling: Robust error detection and reporting
- Typed interfaces: Clear TypeScript types for all data
Key implementation details:
/**
* Manage rate limiting based on free tier (5 calls per minute)
*/
private async enforceRateLimit() {
if (this.requestsThisMinute >= 5) {
console.error("[Rate Limit] Rate limit reached. Waiting for next minute...");
return new Promise<void>((resolve) => {
const remainingMs = 60 * 1000 - (Date.now() % (60 * 1000));
setTimeout(resolve, remainingMs + 100); // Add 100ms buffer
});
}
this.requestsThisMinute++;
return Promise.resolve();
}
We implemented formatters to display financial data beautifully:
/**
* Format company overview into markdown
*/
export function formatStockOverview(overviewData: any, quoteData: any): string {
// Extract data
const overview = overviewData
const quote = quoteData["Global Quote"]
// Calculate price change
const currentPrice = parseFloat(quote["05. price"] || "0")
const priceChange = parseFloat(quote["09. change"] || "0")
const changePercent = parseFloat(quote["10. change percent"]?.replace("%", "") || "0")
// Format markdown
let markdown = `# ${overview.Symbol} (${overview.Name}) - ${formatCurrency(currentPrice)} ${addTrendIndicator(priceChange)}${changePercent > 0 ? "+" : ""}${changePercent.toFixed(2)}%\n\n`
// Add more details...
return markdown
}
We defined five tools with clear interfaces:
server.setRequestHandler(ListToolsRequestSchema, async () => {
console.error("[Setup] Listing available tools")
return {
tools: [
{
name: "get_stock_overview",
description: "Get basic company info and current quote for a stock symbol",
inputSchema: {
type: "object",
properties: {
symbol: {
type: "string",
description: "Stock symbol (e.g., 'AAPL')",
},
market: {
type: "string",
description: "Optional market (e.g., 'US')",
default: "US",
},
},
required: ["symbol"],
},
},
// Additional tools defined here...
],
}
})
Each tool’s handler included:
- Input validation
- API client calls with error handling
- Markdown formatting of responses
- Comprehensive logging
Testing Phase
This critical phase involved systematically testing each tool:
- First, we configured the MCP server in the settings:
{
"mcpServers": {
"alphaadvantage-mcp": {
"command": "node",
"args": ["/path/to/alphaadvantage-mcp/build/index.js"],
"env": {
"ALPHAVANTAGE_API_KEY": "YOUR_API_KEY"
},
"disabled": false,
"autoApprove": []
}
}
}
- Then we tested each tool individually:
-
get_stock_overview: Retrieved AAPL stock overview information
# AAPL (Apple Inc) - $241.84 ↑+1.91%
**Sector:** TECHNOLOGY
**Industry:** ELECTRONIC COMPUTERS
**Market Cap:** 3.63T
**P/E Ratio:** 38.26
...
-
get_technical_analysis: Obtained price action and RSI data
# Technical Analysis: AAPL
## Daily Price Action
Current Price: $241.84 (↑$4.54, +1.91%)
### Recent Daily Prices
| Date | Open | High | Low | Close | Volume |
| ---------- | ------- | ------- | ------- | ------- | ------ |
| 2025-02-28 | $236.95 | $242.09 | $230.20 | $241.84 | 56.83M |
...
-
get_earnings_report: Retrieved MSFT earnings history and formatted report
# Earnings Report: MSFT (Microsoft Corporation)
**Sector:** TECHNOLOGY
**Industry:** SERVICES-PREPACKAGED SOFTWARE
**Current EPS:** $12.43
## Recent Quarterly Earnings
| Quarter | Date | EPS Estimate | EPS Actual | Surprise % |
| ---------- | ---------- | ------------ | ---------- | ---------- |
| 2024-12-31 | 2025-01-29 | $3.11 | $3.23 | ↑4.01% |
...
Challenges and Solutions
During development, we encountered several challenges:
- API Rate Limiting:
- Challenge: Free tier limited to 5 calls per minute
- Solution: Implemented queuing, enforced rate limits, and added comprehensive caching
- Data Formatting:
- Challenge: Raw API data not user-friendly
- Solution: Created formatting utilities for consistent display of financial data
- Timeout Issues:
- Challenge: Complex tools making multiple API calls could timeout
- Solution: Suggested breaking complex tools into smaller pieces, optimizing caching
Lessons Learned
Our AlphaAdvantage implementation taught us several key lessons:
- Plan for API Limits: Understand and design around API rate limits from the beginning
- Cache Strategically: Identify high-value caching opportunities to improve performance
- Format for Readability: Invest in good data formatting for improved user experience
- Test Every Path: Test all tools individually before completion
- Handle API Complexity: For APIs requiring multiple calls, design tools with simpler scopes
Core Implementation Best Practices
Comprehensive Logging
Effective logging is essential for debugging MCP servers:
// Start-up logging
console.error("[Setup] Initializing AlphaAdvantage MCP server...")
// API request logging
console.error(`[API] Getting stock overview for ${symbol}`)
// Error handling with context
console.error(`[Error] Tool execution failed: ${error.message}`)
// Cache operations
console.error(`[Cache] Using cached data for: ${cacheKey}`)
Strong Typing
Type definitions prevent errors and improve maintainability:
export interface AlphaAdvantageConfig {
apiKey: string
cacheTTL?: Partial<typeof DEFAULT_CACHE_TTL>
baseURL?: string
}
/**
* Validate that a stock symbol is provided and looks valid
*/
function validateSymbol(symbol: unknown): asserts symbol is string {
if (typeof symbol !== "string" || symbol.trim() === "") {
throw new McpError(ErrorCode.InvalidParams, "A valid stock symbol is required")
}
// Basic symbol validation (letters, numbers, dots)
const symbolRegex = /^[A-Za-z0-9.]+$/
if (!symbolRegex.test(symbol)) {
throw new McpError(ErrorCode.InvalidParams, `Invalid stock symbol: ${symbol}`)
}
}
Intelligent Caching
Reduce API calls and improve performance:
// Default cache TTL in seconds
const DEFAULT_CACHE_TTL = {
STOCK_OVERVIEW: 60 * 60, // 1 hour
TECHNICAL_ANALYSIS: 60 * 30, // 30 minutes
FUNDAMENTAL_ANALYSIS: 60 * 60 * 24, // 24 hours
EARNINGS_REPORT: 60 * 60 * 24, // 24 hours
NEWS: 60 * 15, // 15 minutes
}
// Check cache first
const cachedData = this.cache.get<T>(cacheKey)
if (cachedData) {
console.error(`[Cache] Using cached data for: ${cacheKey}`)
return cachedData
}
// Cache successful responses
this.cache.set(cacheKey, response.data, cacheTTL)
Graceful Error Handling
Implement robust error handling that maintains a good user experience:
try {
switch (request.params.name) {
case "get_stock_overview": {
// Implementation...
}
// Other cases...
default:
throw new McpError(ErrorCode.MethodNotFound, `Unknown tool: ${request.params.name}`)
}
} catch (error) {
console.error(`[Error] Tool execution failed: ${error instanceof Error ? error.message : String(error)}`)
if (error instanceof McpError) {
throw error
}
return {
content: [
{
type: "text",
text: `Error: ${error instanceof Error ? error.message : String(error)}`,
},
],
isError: true,
}
}
MCP Resources
Resources let your MCP servers expose data to Cline without executing code. They’re perfect for providing context like files, API responses, or database records that Cline can reference during conversations.
Adding Resources to Your MCP Server
- Define the resources your server will expose:
server.setRequestHandler(ListResourcesRequestSchema, async () => {
return {
resources: [
{
uri: "file:///project/readme.md",
name: "Project README",
mimeType: "text/markdown",
},
],
}
})
- Implement read handlers to deliver the content:
server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
if (request.params.uri === "file:///project/readme.md") {
const content = await fs.promises.readFile("/path/to/readme.md", "utf-8")
return {
contents: [
{
uri: request.params.uri,
mimeType: "text/markdown",
text: content,
},
],
}
}
throw new Error("Resource not found")
})
Resources make your MCP servers more context-aware, allowing Cline to access specific information without requiring you to copy/paste. For more information, refer to the official documentation.
Common Challenges and Solutions
API Authentication Complexities
Challenge: APIs often have different authentication methods.
Solution:
- For API keys, use environment variables in the MCP configuration
- For OAuth, create a separate script to obtain refresh tokens
- Store sensitive tokens securely
// Authenticate using API key from environment
const API_KEY = process.env.ALPHAVANTAGE_API_KEY
if (!API_KEY) {
console.error("[Error] Missing ALPHAVANTAGE_API_KEY environment variable")
process.exit(1)
}
// Initialize API client
const apiClient = new AlphaAdvantageClient({
apiKey: API_KEY,
})
Missing or Limited API Features
Challenge: APIs may not provide all the functionality you need.
Solution:
- Implement fallbacks using available endpoints
- Create simulated functionality where necessary
- Transform API data to match your needs
API Rate Limiting
Challenge: Most APIs have rate limits that can cause failures.
Solution:
- Implement proper rate limiting
- Add intelligent caching
- Provide graceful degradation
- Add transparent errors about rate limits
if (this.requestsThisMinute >= 5) {
console.error("[Rate Limit] Rate limit reached. Waiting for next minute...")
return new Promise<void>((resolve) => {
const remainingMs = 60 * 1000 - (Date.now() % (60 * 1000))
setTimeout(resolve, remainingMs + 100) // Add 100ms buffer
})
}
Additional Resources