As AI agents become more common in business applications, the need for reliable communication protocols grows. MCP (Model Context Protocol) servers provide a structured way for AI agents to interact with tools and resources. This article explains what MCP servers are, how they compare to traditional REST APIs, and what to consider when implementing them.
Understanding MCP Servers
An MCP server is a standardized interface that allows AI agents to discover, access, and use tools and resources. Unlike traditional APIs that require custom integration code for each service, MCP servers follow a common protocol. This means agents can connect to different tools using the same approach, reducing complexity and improving reliability.
MCP servers expose three main types of resources: tools (functions the agent can call), resources (data the agent can read), and prompts (templates for common tasks). This structure gives agents a clear understanding of what they can do and how to do it.
MCP Server vs REST API: Key Differences
| Aspect | MCP Server | REST API |
|---|---|---|
| Protocol | Standardized protocol for AI agents | HTTP-based, custom endpoints |
| Discovery | Self-describing (tools, resources, prompts) | Requires documentation or OpenAPI spec |
| Use Case | AI agent interactions | General application integration |
| Complexity | Lower for agent integration | Varies by API design |
| Error Handling | Structured error responses | HTTP status codes |
When to Use MCP Servers
MCP servers are ideal when building AI agent systems that need to interact with multiple tools. They simplify agent development because agents can discover capabilities automatically. This is especially valuable in environments where tools change frequently or when you want agents to adapt to new resources without code changes.
When REST APIs Are Still Appropriate
REST APIs remain the better choice for traditional application-to-application communication, web services, and when you need fine-grained control over request/response formats. They are also more widely supported and have extensive tooling and documentation available.
Do's: Best Practices for MCP Servers
✅ Do's
Provide Clear Tool Descriptions
Each tool should have a clear name, description, and parameter documentation. This helps agents understand when and how to use the tool correctly.
Implement Proper Error Handling
Return structured error messages that explain what went wrong and suggest how to fix it. This helps agents recover from errors gracefully.
Use Resource Versioning
When exposing resources, include version information. This allows agents to understand what data format to expect and helps with compatibility over time.
Validate Input Parameters
Always validate and sanitize inputs before processing. This prevents errors and security issues from reaching your backend systems.
Implement Rate Limiting
Protect your MCP server from abuse by implementing rate limits. This ensures fair usage and prevents system overload.
Log All Agent Interactions
Maintain detailed logs of tool calls, resource access, and errors. This is essential for debugging, security audits, and understanding agent behavior.
Don'ts: Common Mistakes to Avoid
❌ Don'ts
Don't Expose Sensitive Operations Without Authentication
Never allow agents to perform destructive or sensitive operations without proper authentication and authorization checks. Always verify permissions before executing actions.
Don't Return Raw Error Messages
Avoid exposing internal system details in error messages. Return user-friendly errors that don't reveal implementation details or sensitive information.
Don't Create Too Many Small Tools
Avoid creating hundreds of tiny tools. Instead, group related functionality into well-designed tools that handle multiple related tasks. This reduces complexity for agents.
Don't Ignore Timeout Handling
Always implement timeouts for long-running operations. Agents may wait indefinitely if operations don't complete, leading to poor user experience.
Don't Skip Input Validation
Never trust input from agents without validation. Malformed or malicious input can cause system failures or security vulnerabilities.
Don't Mix MCP and REST Without Clear Boundaries
If you need both MCP and REST endpoints, keep them clearly separated. Don't try to make REST APIs work like MCP servers or vice versa. Use each for its intended purpose.
Tool Curation: Selecting and Organizing Tools for MCP Servers
Tool curation is the process of carefully selecting, organizing, and managing which tools an MCP server exposes to AI agents. Good curation ensures agents have access to the right capabilities without being overwhelmed by irrelevant or redundant options.
Why Tool Curation Matters
Without proper curation, agents face several problems: too many similar tools create confusion, irrelevant tools waste processing time, and poorly organized tools make it difficult for agents to find the right capability. Effective curation improves agent performance, reduces errors, and makes systems easier to maintain.
Principles of Effective Tool Curation
1. Group Related Functionality
Instead of exposing dozens of individual tools, combine related operations into cohesive tools. For example, a "file_operations" tool might handle reading, writing, and listing files rather than having separate tools for each action. This reduces cognitive load for agents.
2. Prioritize Common Use Cases
Focus on tools that address frequent agent needs. Tools used rarely should be deprioritized or moved to separate MCP servers. This keeps the primary server focused and efficient.
3. Maintain Clear Naming Conventions
Use consistent, descriptive names for tools. Follow patterns like "verb_noun" (e.g., "send_email", "create_document") to help agents understand tool purposes quickly. Avoid abbreviations unless they are universally understood.
4. Version Your Tools
When tools evolve, maintain version information. This allows agents to understand what capabilities are available and helps with backward compatibility. Consider deprecating old versions gradually rather than removing them abruptly.
5. Document Tool Dependencies
Some tools may depend on others or require specific resources to be available. Document these relationships clearly so agents understand prerequisites and can plan their workflows accordingly.
6. Monitor Tool Usage
Track which tools agents actually use. Tools that are never called might be unnecessary or poorly documented. Regular usage analysis helps refine your tool set over time.
7. Balance Granularity
Find the right balance between too many small tools and too few large ones. Tools should be specific enough to be useful but general enough to handle related tasks. Aim for tools that solve complete problems rather than partial ones.
Tool Curation Workflow
A systematic approach to curation involves several steps:
- Inventory existing tools: List all available tools and their purposes. Identify overlaps and gaps.
- Analyze agent behavior: Review logs to see which tools agents actually use and how often.
- Group and consolidate: Combine related tools and remove redundant ones.
- Document and test: Ensure all tools have clear descriptions and test that agents can discover and use them effectively.
- Iterate based on feedback: Monitor usage patterns and adjust your tool set as needs evolve.
Example: Curated Tool Set for a Business Automation MCP Server
Instead of exposing 50 individual tools, a well-curated business automation server might offer:
- document_management: Create, read, update, and search documents
- communication: Send emails, create calendar events, send messages
- data_analysis: Query databases, generate reports, visualize data
- workflow_automation: Trigger processes, manage approvals, track status
Each tool handles a complete domain of functionality, making it easier for agents to understand and use the system effectively.
Real-World Example: MCP Server Implementation
Consider a file management MCP server. It might expose tools like "read_file", "write_file", and "list_directory". Each tool has clear parameters: file paths, content, and permissions. The server validates paths, checks permissions, and returns structured results. An AI agent can discover these tools automatically and use them without custom integration code.
In contrast, a REST API for the same functionality would require the agent to know specific endpoint URLs, HTTP methods, and response formats. The agent developer would need to write custom code for each operation.
Conclusion
MCP servers provide a standardized way for AI agents to interact with tools and resources. They reduce integration complexity and make agent systems more maintainable. While REST APIs remain essential for general application integration, MCP servers are becoming the preferred choice for AI agent architectures.
When implementing MCP servers, focus on clear documentation, proper error handling, and security. Avoid common pitfalls like exposing sensitive operations without protection or creating overly complex tool structures. With careful design, MCP servers can significantly improve the reliability and efficiency of AI agent systems.
Need Help Implementing MCP Servers?
Our team specializes in building robust AI agent systems with MCP integration. Let's discuss how we can help you implement MCP servers for your use case.
Book a Consultation