Prompts for Managing Roots in a Kafka‑MCP‑Server

Roots let clients define which URIs the server should focus on—filesystem paths, API endpoints, configuration directories, and more. Below are useful prompts (slash‑commands or natural‑language) that you can expose in your Kafka‑MCP‑Server to let users and LLM agents manage roots interactively.

1. Slash‑Command Prompts

• /kafka add-root <uri> name="<display-name>" “Register a new root at <uri> with the name <display-name>.”

• /kafka list-roots “Show all currently registered roots (URI and name).”

• /kafka update-root <uri> name="<new-name>" “Change the display name of the root at <uri> to <new-name>.”

• /kafka remove-root <uri> “Unregister the root located at <uri>.”

2. Natural‑Language Prompts

• “Add a root for my local config directory: file:///home/user/kafka/config named ‘Broker Configs’.”

• “List all roots you’re using right now.”

• “Remove the API endpoint root https://api.example.com/v1.”

• “Rename the root file:///var/logs to ‘Kafka Logs’.”

3. Batch & Initialization Prompts

• “Initialize roots with: • file:///home/user/projects/kafka as ‘Project Repo’ • https://metrics.example.com/api as ‘Metrics API’”

• “Reset roots to only include my current workspace folder.”

4. Validation & Discovery Prompts

• “Validate that all registered roots are accessible.”

• “Suggest roots based on the current working directory.”

• “Which roots contain configuration files?”

5. Examples in JSON Format

Expose these templates for clients that work with JSON payloads:

{
  "roots": [
    { "uri": "file:///home/user/kafka/config", "name": "Broker Configs" },
    { "uri": "https://api.monitoring/v1",   "name": "Monitoring API" }
  ]
}

Best Practices

• Use clear, descriptive display names to help LLMs and users understand purpose.

• Encourage URI validation to alert on unreachable roots.

• Allow batch registration of multiple roots for large workspaces.

• Support dynamic updates so roots can change as projects evolve.

These prompts empower conversational and programmatic control over which data sources and endpoints your Kafka‑MCP‑Server will surface to LLMs, ensuring context‑aware operations within defined boundaries.