MCP Servers: The Complete Guide (2025)

The Model Context Protocol (MCP) has become the standard for connecting AI assistants to external data and tools. Whether you use Claude, ChatGPT, or Cursor, MCP servers extend what your AI can do—from reading files to querying databases to managing cloud infrastructure.

This comprehensive guide covers everything you need to know about MCP servers: what they are, how they work, the best servers available, and how to configure them for your workflow.

MCP (Model Context Protocol) is an open standard created by Anthropic that lets AI assistants connect to external tools and data sources through standardized server implementations. Think of it as "USB-C for AI"—a universal connector that works across different AI clients.

Why MCP Matters

Before MCP, every AI integration was custom. Connecting Claude to GitHub required different code than connecting GPT-4 to the same service. This fragmentation meant:

Duplicate development effort
Inconsistent capabilities across tools
Limited extensibility for end users

MCP changes this. Write one server, use it everywhere—Claude Desktop, Claude Code, Cursor, VS Code, and more.

Industry Adoption

MCP has achieved remarkable adoption:

| Date | Milestone |

|------|-----------|

| Nov 2024 | Anthropic releases MCP |

| Mar 2025 | OpenAI adopts MCP for ChatGPT, Agents SDK |

| Apr 2025 | Google confirms MCP support for Gemini |

| Dec 2025 | MCP donated to Linux Foundation's AAIF |

By late 2025, MCP downloads grew from ~100K to over 8 million, with thousands of servers available.

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How MCP Works

Architecture Overview

MCP uses a client-server architecture:


┌─────────────────┐     JSON-RPC     ┌─────────────────┐
│   AI Client     │ ◄──────────────► │   MCP Server    │
│ (Claude, etc.)  │                  │ (GitHub, etc.)  │
└─────────────────┘                  └─────────────────┘


Client: The AI application (Claude Desktop, Cursor, etc.)
Server: A process that exposes capabilities to the client
Protocol: JSON-RPC 2.0 over stdio or HTTP

Three Core Primitives
MCP servers can expose three types of capabilities:

  
    title="Resources"
    description="Read-only data sources. Files, database records, API responses that the AI can read but not modify."
  />
  
    title="Tools"
    description="Actions the AI can take. Create files, run queries, send messages, deploy code."
  />
  
    title="Prompts"
    description="Pre-built prompt templates with expertise. Not just data, but knowledge about how to use it."
  />

Transport Methods
MCP supports multiple transport mechanisms:
| Transport | Use Case | Description |
|-----------|----------|-------------|
| stdio | Local servers | Standard input/output, most common |
| HTTP/SSE | Remote servers | For cloud-hosted MCP servers |
| WebSocket | Real-time | Bidirectional communication |
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Essential MCP Servers
Here are the most valuable MCP servers across different categories.
File System
Filesystem Server (Official)
Direct access to local files with security controls:

json
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/directory"]
    }
  }
}


Capabilities:
Read and write files
List directory contents
Search file contents
Create and delete files


  Only grant access to directories you trust the AI to modify. The path argument limits access scope.

Version Control
GitHub Server (Official)
Full GitHub integration:

json
{
  "mcpServers": {
    "github": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token"
      }
    }
  }
}


Capabilities:
Repository browsing
Issue and PR management
Code search
Actions workflow monitoring

Git Server (Official)
Local Git operations:

json
{
  "mcpServers": {
    "git": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-git"]
    }
  }
}


Capabilities:
View commit history
Check repository status
Read diffs
Search commits

Databases
PostgreSQL Server
Read-only PostgreSQL access:

json
{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres", "postgresql://user:pass@localhost/db"]
    }
  }
}


Capabilities:
Execute read queries
Inspect schema
List tables and columns

SQLite Server
Local database operations:

json
{
  "mcpServers": {
    "sqlite": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sqlite", "--db-path", "/path/to/database.db"]
    }
  }
}


Communication
Slack Server
Workspace integration:

json
{
  "mcpServers": {
    "slack": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-slack"],
      "env": {
        "SLACK_BOT_TOKEN": "xoxb-your-token",
        "SLACK_TEAM_ID": "T0123456789"
      }
    }
  }
}


Capabilities:
List and read channels
Search messages
Post updates (optional)
Thread management

Cloud & Infrastructure
AWS Server
AWS service management:

json
{
  "mcpServers": {
    "aws": {
      "command": "npx",
      "args": ["-y", "@aws/aws-mcp"],
      "env": {
        "AWS_ACCESS_KEY_ID": "your-key",
        "AWS_SECRET_ACCESS_KEY": "your-secret",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}


DigitalOcean Server
Droplet and app management:

json
{
  "mcpServers": {
    "digitalocean": {
      "command": "npx",
      "args": ["-y", "@digitalocean/mcp-server"],
      "env": {
        "DIGITALOCEAN_ACCESS_TOKEN": "your-token"
      }
    }
  }
}


Web & APIs
Puppeteer Server
Browser automation:

json
{
  "mcpServers": {
    "puppeteer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-puppeteer"]
    }
  }
}


Capabilities:
Navigate websites
Take screenshots
Extract data
Fill forms

Fetch Server
HTTP requests:

json
{
  "mcpServers": {
    "fetch": {
      "command": "npx",
      "args": ["-y", "@anthropic-ai/mcp-fetch"]
    }
  }
}


---
Configuration by Platform
Different AI clients have slightly different configuration formats.
Claude Code

Claude Code uses .claude/mcp.json in your project or ~/.claude/mcp.json globally:

json
{
  "mcpServers": {
    "server-name": {
      "command": "npx",
      "args": ["-y", "package-name"],
      "env": {
        "API_KEY": "your-key"
      }
    }
  }
}


Or use the CLI:

bash
claude mcp add server-name -e API_KEY=your-key -- npx -y package-name


Claude Desktop
Configuration file location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

json
{
  "mcpServers": {
    "server-name": {
      "command": "npx",
      "args": ["-y", "package-name"]
    }
  }
}


Cursor
Access via Settings → MCP, or edit the configuration directly:

json
{
  "mcpServers": {
    "server-name": {
      "command": "npx",
      "args": ["-y", "package-name"]
    }
  }
}



  Cursor only supports MCP tools (not resources), with a 40-tool limit per session. You must be in Agent Mode to use MCP servers.

VS Code
VS Code uses a slightly different format:

json
{
  "mcp": {
    "servers": {
      "server-name": {
        "command": "npx",
        "args": ["-y", "package-name"]
      }
    }
  }
}


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Building Your Own MCP Server
MCP servers can be built in multiple languages. Here's a TypeScript example:
Basic Server Structure

typescript
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
const server = new Server(
  { name: "my-server", version: "1.0.0" },
  { capabilities: { tools: {} } }
);
// Define a tool
server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "my_tool",
    description: "Does something useful",
    inputSchema: {
      type: "object",
      properties: {
        input: { type: "string", description: "The input" }
      },
      required: ["input"]
    }
  }]
}));
// Handle tool calls
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "my_tool") {
    const result = await doSomething(request.params.arguments.input);
    return { content: [{ type: "text", text: result }] };
  }
});
// Start the server
const transport = new StdioServerTransport();
await server.connect(transport);


Available SDKs
| Language | Package |
|----------|---------|

| TypeScript | @modelcontextprotocol/sdk |

| Python | mcp |

| C# | ModelContextProtocol.NET |

| Java | modelcontextprotocol-java |


---
Best MCP Servers by Category
Development & Coding
| Server | Purpose |
|--------|---------|
| GitHub | Repository management, issues, PRs |
| GitLab | GitLab API integration |
| Linear | Issue tracking for engineering teams |
| Sentry | Error monitoring and analysis |
| CAS | Persistent memory and task tracking |
Productivity
| Server | Purpose |
|--------|---------|
| Slack | Team communication |
| Notion | Documentation and wikis |
| Google Drive | File storage and docs |
| Todoist | Task management |
Data & Analytics
| Server | Purpose |
|--------|---------|
| PostgreSQL | Relational database queries |
| SQLite | Local database operations |
| BigQuery | Data warehouse analytics |
| Snowflake | Cloud data platform |
Infrastructure
| Server | Purpose |
|--------|---------|
| AWS | Amazon Web Services |
| DigitalOcean | Cloud infrastructure |
| Cloudflare | Edge and CDN |
| Kubernetes | Container orchestration |
Web & Automation
| Server | Purpose |
|--------|---------|
| Puppeteer | Browser automation |
| Playwright | Cross-browser testing |
| Firecrawl | Web scraping |
| Browserbase | Cloud browser sessions |
---
Security Considerations
MCP servers have access to sensitive systems. Security matters.
Known Risks

  
    title="Prompt Injection"
    description="Malicious content in data sources could manipulate AI behavior. Always validate inputs."
  />
  
    title="Tool Combination"
    description="Combining read and write tools could enable data exfiltration. Limit tool sets carefully."
  />
  
    title="Lookalike Tools"
    description="A malicious server could impersonate a trusted one. Verify server sources."
  />
  
    title="Credential Exposure"
    description="API keys in config files could be exposed. Use environment variables."
  />

Best Practices

  
    Only grant the permissions your workflow actually needs. Read-only where possible.
  
  
    Never hardcode tokens in configuration files. Use env vars or secret managers.
  
  
    Install servers from official repos or trusted publishers. Review code when possible.
  
  
    Review AI actions before execution, especially for write operations.
  
  
    Restrict filesystem access to specific directories. Use read-only database connections.
  

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Finding MCP Servers
Official Resources
MCP Servers GitHub — Official repository with reference implementations
MCP.so — Community server directory
Awesome MCP Servers — Curated list

Marketplaces
Composio — 900+ pre-built servers with managed hosting
MCP Bundles — Curated server collections for specific workflows
Cline Marketplace — Community servers for Cline users

---
CAS: Memory Server for Development
CAS (Coding Agent System) is an MCP server purpose-built for AI-assisted development. Unlike general-purpose memory tools, CAS provides:
Features
| Feature | Description |
|---------|-------------|
| Unified Memory | Store learnings, preferences, and context |
| Task Tracking | Track work with full context restoration |
| Coding Rules | Standards that surface automatically |
| Skills | Captured procedures and workflows |
| Semantic Search | Find relevant context by meaning |
Installation

bash
Add CAS to Claude Code
claude mcp add cas


Usage


Store important context
cas_remember: "This project uses TailwindCSS v4 with @apply for components"
Track tasks
cas_task_create: "Implement user authentication"
Search across everything
cas_search: "How did we handle rate limiting?"

CAS provides the persistent memory that MCP-enabled AI assistants need but don't have natively.

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Troubleshooting

Server Not Appearing

Symptoms: Tools don't show in AI client Solutions:

Restart the AI client after configuration changes
Check JSON syntax in config file
Verify the server command works when run manually
Check for conflicting server names

Connection Errors

Symptoms: "Failed to connect to MCP server" Solutions:

Ensure Node.js is installed for npx servers
Verify Docker is running for Docker-based servers
Check environment variables are set correctly
Look for port conflicts

Permission Denied

Symptoms: Server can't access resources Solutions:

Verify API tokens have correct scopes
Check file system permissions for local access
Ensure network access isn't blocked

Slow Performance

Symptoms: Long delays when using MCP tools Solutions:

Use local servers where possible (vs remote)
Consider caching frequently-accessed data
Limit the number of active servers

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Future of MCP

The MCP ecosystem is evolving rapidly:

Emerging Trends

Agentic AI Foundation (AAIF)

In December 2025, Anthropic donated MCP to AAIF under the Linux Foundation. This neutral governance ensures:

Open development
Industry-wide input
Long-term sustainability

Standardization

With OpenAI and Google adopting MCP, it's becoming the true standard for AI tool integration. Expect:

More official servers from major platforms
Better security specifications
Improved authentication standards

Growth

The MCP market is projected to grow from $2.7B in 2025 to $5.5B by 2034—a 8.3% CAGR. This growth means:

More commercial servers
Better tooling
Enterprise-grade features

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Conclusion

MCP servers are the bridge between AI assistants and the real world. They turn chat-based AI into capable agents that can read your data, interact with your tools, and take actions on your behalf.

Key takeaways:

Start simple — Add one or two servers that solve immediate needs
Security first — Limit permissions, use environment variables
Choose verified sources — Stick to official and well-reviewed servers
Consider CAS — For development workflows, persistent memory is essential

The MCP ecosystem will only grow. By understanding it now, you're positioning yourself to leverage AI assistants to their full potential.

Start with the GitHub and Filesystem servers—they cover most development workflows. Add CAS for persistent memory across sessions. Then expand based on your specific needs.

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