How to Build MCP Tools That Don't Drive You Crazy with Conflicts

The Sneaky Ways MCP Tools Can Clash

1. The "Who are you again?" Problem: Naming Collisions & Shadowing

2. The "I'm confused" Issue: Slash Command Overlaps

3. The "Trust me, I'm a good guy" Deception: Tool Poisoning & Rug-Pulls

4. The "We're running out of space!" Conflict: Resource & Performance Issues

• Tool Budget Overload: Docker's team has a great internal term for this: "tool budget." It's the number of tools an AI agent can effectively handle. If you create an MCP server that exposes every single endpoint of a complex API as a separate tool, you can overwhelm the AI. It makes the server more complex, more expensive to run, & harder for the AI to choose the right tool.
• Context Window Abuse: LLMs have a limited context window—the amount of information they can "remember" at one time. If your MCP tool returns a massive amount of data (like the entire text of a book when the user just wanted a summary), it can clog up the context window. This makes the AI less effective, slows down response times, & can even cause it to "forget" what it was doing.
• Performance Bottlenecks: An MCP server that's slow or inefficient can bring an entire AI workflow to a halt. This is especially common when dealing with large datasets. Without things like pagination, caching, & asynchronous processing, a server can become a major bottleneck.

5. The "It worked yesterday" Syndrome: API Drift & Maintenance

How to Build Harmonious MCP Tools: Your Guide to Conflict-Free Development

Designing for the REAL User: The AI

• Write Crystal-Clear Descriptions: The tool's name & description are the AI's main guide. Be explicit & descriptive.
  • Use Verbs in Tool Names: Instead of
    1 data_fetcher
    , use
    1 fetch_user_data
    . It's more action-oriented.
  • Explain What & Why: Don't just say "gets user data." Say "gets user profile information like name, email, & sign-up date from the main database."
  • Add Examples: Include examples in your parameter descriptions. This helps the AI understand what kind of input you're expecting.
• Manage Your Tool Budget Wisely: Don't just map every API endpoint to a tool. Think about the key use cases. Group related functionalities into a single, more powerful tool. For example, instead of having

  1 create_user

  ,

  1 update_user

  , &

  1 delete_user

  , you could have a single

  1 manage_user

  tool with an

  1 action

  parameter.
• Think in Self-Contained Units: Each tool call should be as self-contained as possible. For example, instead of establishing a database connection when the server starts, create a new connection for each tool call. This might feel inefficient, but it makes the server more robust & allows users to list tools even if the server isn't fully configured.

Server-Side Best Practices: Building a Fortress

• Security is NOT Optional:
  • Authentication is a MUST: The MCP spec recommends OAuth 2.1 for a reason. Don't skip it. A huge number of exposed MCP servers have been found with no authentication at all. Implement proper token validation on every single request.
  • Principle of Least Privilege: Your server should run with the absolute minimum permissions it needs. Don't give it root access. Don't let it access the whole file system if it only needs to read one folder.
  • Sandbox Untrusted Code: If your tool needs to run code (e.g., a code interpreter), do it in a sandboxed environment like a Docker container or WebAssembly. This prevents it from breaking out & affecting the rest of your system.
  • Log EVERYTHING: Log all tool calls, inputs, outputs, timestamps, & user approvals. If something goes wrong, these logs will be your best friend. No logs means you have no idea what the AI just did.
• Performance & Reliability:
  • Implement Pagination: For any tool that can return a list of items, add pagination. Never return thousands of results in one go.
  • Use Caching: Cache frequently accessed data to speed up response times.
  • Create Summary Endpoints: If a tool deals with large objects (like a long document), create a separate "summary" endpoint that returns just the metadata or a small snippet. This saves the AI's context window.
• Documentation for Humans AND Agents: You need two layers of documentation. The first is the in-code descriptions for the AI. The second is human-readable documentation (like a README) that explains what the server does, how to set it up, & what the security considerations are.

Client-Side Best Practices: The Responsible Consumer

• Connect Securely: Always use TLS when connecting to remote MCP servers. Validate & sanitize all inputs before sending them off.
• Handle Errors Gracefully: Things will break. Servers will go down. Implement retry logic with exponential backoff so you don't overwhelm a struggling server. Have sensible timeouts.
• Explicit User Consent is CRITICAL: For any action that modifies data, spends money, or sends information, get explicit confirmation from the user. Don't let the AI run wild. This is especially important for businesses building customer-facing AI.

• Request Only What You Need: Don't just ask for every permission under the sun. Follow the principle of least privilege. If your client only needs to read files, don't ask for write access.
• Trust, but Verify: Whenever possible, encourage users to use official MCP servers from trusted vendors. Be wary of third-party proxies that could be snooping on your data.

Tying It All Together