Frequently Asked Questions¶

Quick answers to common questions about the Model Context Protocol.

General Questions¶

What is MCP?¶

The Model Context Protocol (MCP) is an open standard that enables AI models to interact with external tools and data sources through a unified interface. It's like USB for AI - one protocol that works everywhere.

Who created MCP?¶

MCP was developed and open-sourced by Anthropic to solve the fragmentation problem in AI integrations. It's now maintained by the open-source community.

Is MCP free to use?¶

Yes! MCP is completely open-source and free to use. The protocol specification, SDKs, and many server implementations are available under permissive licenses.

What AI models support MCP?¶

Currently, Claude Desktop supports MCP natively. More AI platforms are adopting MCP, and any AI system can add MCP support using the available SDKs.

Technical Questions¶

What programming languages can I use?¶

MCP has official SDKs for: - Python - TypeScript/JavaScript - Rust (community) - Go (community)

The protocol is language-agnostic, so you can implement it in any language that supports JSON-RPC.

How does MCP differ from REST APIs?¶

Can I use MCP for real-time applications?¶

Yes! MCP supports WebSocket transport for real-time, bidirectional communication. You can stream data, receive notifications, and maintain persistent connections.

What's the performance overhead?¶

MCP adds minimal overhead: - JSON-RPC parsing: ~1ms - Schema validation: ~2-5ms - Transport latency: Depends on transport type

For most applications, the benefits far outweigh the minimal performance cost.

Implementation Questions¶

How do I get started?¶

1. Choose your role: Building a server (tool provider) or client (AI application)?
2. Pick an SDK for your preferred language
3. Follow our Quick Start Guide
4. Start with a simple "Hello World" implementation

Can I convert my existing API to MCP?¶

Absolutely! You can wrap existing APIs with an MCP server:

@server.tool()
async def call_my_api(endpoint: str, params: dict):
    # Call your existing API
    response = await my_api.call(endpoint, params)
    return TextContent(text=json.dumps(response))

How do I handle authentication?¶

MCP supports multiple authentication methods: - API keys via headers - OAuth 2.0 flows - mTLS for high-security environments - Custom authentication schemes

Can MCP servers call other MCP servers?¶

Yes! An MCP server can act as a client to other servers, enabling powerful composition patterns and microservice architectures.

Security Questions¶

Is MCP secure?¶

MCP includes several security features: - Transport encryption (TLS) - Authentication mechanisms - Input validation via JSON Schema - Rate limiting support - Audit logging capabilities

How do I restrict what clients can do?¶

Implement authorization in your server:

@server.tool()
async def sensitive_operation(client_id: str):
    if not has_permission(client_id, "sensitive_op"):
        raise PermissionError("Unauthorized")
    # Proceed with operation

Can I use MCP in production?¶

Yes! MCP is production-ready. Consider: - Using HTTPS/WSS transports - Implementing proper authentication - Adding rate limiting - Monitoring and logging - Following our Security Best Practices

Business Questions¶

What's the ROI of implementing MCP?¶

Organizations typically see: - 60-80% reduction in integration development time - 50% lower maintenance costs - 3x faster time-to-market for AI features - Future-proof architecture (new AIs work automatically)

Do I need to rewrite everything?¶

No! You can: 1. Start with one tool or service 2. Wrap existing APIs with MCP 3. Gradually migrate other integrations 4. Keep non-AI integrations as-is

What about vendor lock-in?¶

MCP actually prevents vendor lock-in: - Open standard with no licensing fees - Switch AI providers without changing integrations - Community-driven development - Multiple implementations available

Is there commercial support?¶

While MCP itself is open-source, several companies offer: - Managed MCP hosting - Implementation consulting - Enterprise support packages - Custom development services

Troubleshooting¶

My server isn't connecting¶

Check these common issues: 1. Transport mismatch - Ensure client and server use same transport 2. Path issues - Verify stdio server path is correct 3. Permissions - Check file/network permissions 4. Firewall - Ensure ports are open for network transports

Tools aren't appearing in my client¶

Verify: - Server implements tools/list method - Tools are properly decorated/registered - Client has tools capability enabled - No errors during initialization

I'm getting schema validation errors¶

Common fixes: - Ensure arguments match exact schema types - Check required vs optional fields - Validate JSON Schema is properly formatted - Test with minimal arguments first

Performance is slow¶

Optimize by: - Using batch requests for multiple operations - Implementing caching where appropriate - Choosing efficient transport (stdio for local) - Profiling server-side operations

Community Questions¶

How can I contribute?¶

We welcome contributions: - Submit bug reports and feature requests - Improve documentation - Create new server implementations - Share your use cases - Help others in discussions

Where can I get help?¶

• Read our comprehensive documentation
• Check GitHub Discussions
• Review existing issues
• Join community forums

Are there example implementations?¶

Yes! Check out: - Our Samples section for tutorials - GitHub topic for community servers - Official SDK examples - Production use cases