Effective AI Collaboration
With your AI assistant configured (Chapter 15), this chapter focuses on making your collaboration productive. We cover the cargo-pmcp workflow, effective prompting strategies, and quality assurance patterns.
The Collaboration Model
┌─────────────────────────────────────────────────────────────────────────┐
│ Effective AI Collaboration │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ You (Developer) │ │
│ │ │ │
│ │ • Define WHAT to build (business requirements) │ │
│ │ • Provide domain knowledge (API constraints, data models) │ │
│ │ • Make architectural decisions (transport, security) │ │
│ │ • Review generated code (ownership and understanding) │ │
│ └──────────────────────────┬──────────────────────────────────────┘ │
│ │ │
│ Clear Communication │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ AI Assistant │ │
│ │ │ │
│ │ • Generates HOW to build (code implementation) │ │
│ │ • Applies cargo-pmcp patterns (scaffolding, testing) │ │
│ │ • Handles boilerplate (types, error handling, serialization) │ │
│ │ • Iterates on compiler feedback (until quality gates pass) │ │
│ └──────────────────────────┬──────────────────────────────────────┘ │
│ │ │
│ Quality Validation │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Rust Compiler + Tooling │ │
│ │ │ │
│ │ • Type checking (catches errors at compile time) │ │
│ │ • Borrow checking (memory safety guarantees) │ │
│ │ • Clippy linting (code quality enforcement) │ │
│ │ • Test runner (behavior verification) │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘
The Three Pillars
1. Structured Workflow
A predictable workflow reduces ambiguity:
Request → Scaffold → Implement → Validate → Deploy
│ │ │ │
└─────────┴──────────┴───────────┘
AI handles these steps
You provide direction
2. Effective Communication
Good prompts lead to good code:
| Poor Prompt | Better Prompt |
|---|---|
| "Make an API server" | "Create an MCP server that queries the GitHub API" |
| "Add database stuff" | "Add a list_tables tool that returns table names and row counts" |
| "Fix the bug" | "The get_user tool returns 500 when the user doesn't exist. It should return a validation error." |
3. Quality Enforcement
Automated quality gates catch issues:
# AI iterates until all pass
cargo fmt --check # ✓ Formatting
cargo clippy # ✓ Linting
cargo test # ✓ Unit tests
cargo pmcp test # ✓ Integration tests
What Makes This Different
Traditional AI Code Generation
Prompt → Generate → Deploy → Runtime Errors → Debug → Repeat
^ |
|________________________|
Slow feedback
MCP Development with pmcp
Prompt → Scaffold → Generate → Compile → Fix → Validate → Deploy
^ | | |
| └──────────┴────────┘
| Fast iteration
└── cargo-pmcp handles structure
Key differences:
- Structure is given - cargo-pmcp scaffolds correctly
- Errors caught early - Rust compiler prevents runtime bugs
- AI can self-correct - Compiler feedback enables iteration
- Quality is enforced - Gates prevent bad code from shipping
Division of Responsibilities
You Are Responsible For
-
Requirements Definition
- What tools should the server provide?
- What data should be accessible?
- What are the error cases?
-
Domain Knowledge
- API authentication methods
- Data validation rules
- Business logic constraints
-
Architectural Decisions
- Transport mode (stdio vs HTTP)
- Security requirements
- Deployment target
-
Code Review
- Understanding what was generated
- Catching logical errors
- Ensuring maintainability
AI Is Responsible For
-
Code Generation
- Type definitions
- Handler implementations
- Error handling boilerplate
-
Pattern Application
- TypedTool structure
- JsonSchema derives
- cargo-pmcp conventions
-
Iteration
- Fixing compiler errors
- Addressing clippy warnings
- Updating failing tests
-
Documentation
- Inline comments
- API documentation
- Usage examples
Working Sessions
Short Sessions (15-30 minutes)
Good for:
- Adding a single tool
- Fixing a specific bug
- Updating existing functionality
Pattern:
"Add a search_users tool to the GitHub server that takes
a query string and returns matching usernames"
Medium Sessions (1-2 hours)
Good for:
- Creating a new server
- Implementing a feature set
- Major refactoring
Pattern:
"Create a PostgreSQL MCP server with:
1. list_tables - returns table names
2. describe_table - returns column info
3. query - runs SELECT with row limit
4. explain - shows query plan
Use sqlx for async database access."
Long Sessions (half day+)
Good for:
- Complex multi-server projects
- Full feature implementation
- Learning new patterns
Pattern:
"Build a complete CI/CD MCP server that:
1. Monitors GitHub Actions workflows
2. Triggers deployments
3. Provides status resources
4. Implements approval workflows
Break this into phases. Start with read-only
monitoring, then add write capabilities."
Anti-Patterns to Avoid
1. Micromanaging Implementation
Bad:
"Create a struct called WeatherInput with a field city
of type String. Then create another struct called..."
Good:
"Create a weather tool that fetches current temperature
for a city. Return temperature in Celsius."
Let AI handle implementation details.
2. Vague Requirements
Bad:
"Make a database thing"
Good:
"Create a SQLite MCP server with list_tables and
execute_query tools. Limit queries to SELECT only."
Be specific about capabilities.
3. Ignoring Compiler Feedback
Bad:
User: "That doesn't work"
AI: "Let me try something else entirely"
Good:
User: "Here's the compiler error: [error message]"
AI: "I see the issue - the lifetime annotation is wrong.
Let me fix that specific problem."
Share error messages for targeted fixes.
4. Skipping Quality Gates
Bad:
User: "Just make it compile, we'll fix warnings later"
Good:
User: "Run cargo clippy and fix all warnings before
we consider this done"
Maintain quality throughout.
Chapter Overview
This chapter covers three key topics:
The Development Workflow
The step-by-step cargo-pmcp workflow:
- Creating workspaces
- Adding servers
- Implementing tools
- Testing and validation
- Production deployment
Prompting for MCP Tools
Effective communication strategies:
- Describing tool requirements
- Specifying input/output types
- Handling error cases
- Iterating on generated code
Quality Assurance with AI
Ensuring production-quality output:
- Automated quality gates
- Test generation
- Code review patterns
- Common issue resolution
Summary
Effective AI collaboration requires:
- Clear communication - Specific requirements, domain context
- Structured workflow - cargo-pmcp patterns, predictable steps
- Quality enforcement - Automated gates, compiler feedback
- Appropriate division - You decide what, AI implements how
The goal is productive partnership: you provide direction and domain expertise, AI handles implementation details and iteration. The Rust compiler serves as an impartial referee, catching errors before they become bugs.
Knowledge Check
Test your understanding of AI collaboration patterns:
Continue to The Development Workflow →