Core Technical Skills Required
Programming & Frameworks
- Python (essential - 95% of orchestration work)
- Agent frameworks experience: LangChain, CrewAI, AutoGen, or LangGraph
- Async programming and event-driven architectures
- API design (REST, gRPC) and integration
AI/ML Fundamentals
- LLM integration and prompt engineering
- Understanding of different model capabilities (GPT-4, Claude, etc.)
RAG (Retrieval Augmented Generation) systemsPosition Overview
An internship focused on building, testing, and deploying Model Context Protocol (MCP) servers and Agent-to-Agent (A2A) communication protocols for distributed, multi-agent AI systems. Interns will design MCP tool ecosystems, implement A2A messaging patterns, handle agent discovery, manage inter-agent communication flows, and ensure secure, stateful agent orchestration across enterprise environments.
Core Focus: MCP (vertical integration of tools/context) + A2A (horizontal agent collaboration)
Duration: 3-6 months (full-time)
Target Experience Level: Final-year B.Tech, M.Tech, or professionals with prior agentic systems exposure
Key ResponsibilitiesMCP Server Development (40% of time)
- MCP Server Architecture: Design and implement MCP servers from scratch using Python or JavaScript MCP SDKs, not relying on higher-level abstractions
- Tool Definition & Schema: Define stateless, JSON-Schema-compliant tools with proper input/output contracts
- Resource Management: Expose file-like resources (APIs, databases, documents) through MCP resource handlers
- Prompt Templates: Build reusable MCP prompts for specific agent use cases
- Multi-Server Orchestration: Coordinate multiple MCP servers to a single client agent; implement context injection and request interception
- FastMCP Optimization: Use FastMCP to generate tool definitions from Python type hints and docstrings
- Error Handling & Resilience: Build robust error recovery, timeout management, and fallback mechanisms
A2A Protocol Implementation (40% of time)
- A2A Client/Server Architecture: Build A2A-capable agents (client agents for task delegation, remote agents for execution)
- Agent Discovery: Implement dynamic agent discovery using Agent Cards (capability advertisement)
- Task Delegation & Lifecycle Management: Design task request/response flows, task status tracking, and lifecycle events
- Streaming & Async Communication: Implement async task streaming, intermediate result broadcasting, and event-driven architectures
- A2A Security & Auth: Design authorization flows, capability-based delegation, and secure agent-to-agent credential exchange
- HTTP/MQTT Transport: Support A2A over HTTP (request/response) and MQTT (pub/sub) transports
- Distributed Tracing: Integrate W3C trace context headers, log correlation (task IDs, trace IDs, session IDs), and metrics instrumentation
Integration & Hybrid Patterns (15% of time)
- MCP + A2A Synergy: Build systems where agents collaborate via A2A and invoke external tools via MCP
- Real-World Workflows: Design multi-agent workflows (e.g., travel booking: Booking Agent + Calendar Agent + Payment Agent, each using MCP for external services)
- Protocol Bridging: Handle communication between legacy systems and MCP/A2A architectures
- Testing & Validation: Build end-to-end tests for MCP tool correctness and A2A agent communication reliability
Evaluation & Documentation (5% of time)
- Document MCP server capabilities, tool schemas, and integration examples
- Create A2A protocol diagrams, agent card definitions, and capability matrices
- Build example repositories and deployment guides
- Performance profiling and latency analysis
Required Qualifications
Technical Core (Must Have)
Python/JavaScript Proficiency:
- Advanced Python (async/await, type hints, class design) or JavaScript/Node.js
- Comfortable implementing servers, not just scripting
- Familiarity with threading/async concurrency patterns
API Design & JSON-RPC:
- Understanding of stateless vs. stateful protocols
- JSON-RPC 2.0 fundamentals
- Request/response serialization and error handling
- Type safety via JSON Schema
HTTP & Network Protocols:
- Fluent with REST APIs, HTTP methods, headers, status codes
- Experience with async HTTP clients (httpx, aiohttp, axios)
- Basic networking concepts (TCP, DNS, load balancing)
- Optional: MQTT publish/subscribe architecture
Async & Distributed Systems:
- Event-driven architectures and message queues
- Understanding of distributed logging (correlation IDs, trace context)
- Basic experience with webhooks or event streaming
- Timeout, retry, and circuit breaker patterns
Server Implementation:
- Built custom servers before (not just using frameworks)
- Experience with server lifecycle management (startup, shutdown, graceful degradation)
- Load handling and connection pooling concepts
AI/ML Foundations
- Agentic Systems Understanding: Familiarity with agent architectures, tool calling, and orchestration
- LLM Integration: Understanding of how agents invoke tools and integrate results
- Multi-Agent Workflows: Grasp of task delegation, capability-based routing, and agent coordination
- Security & Privacy: Understanding of PII handling, data minimization, and encryption for agent payloads
Soft Skills
- Clear technical documentation and explanation abilities
- Comfort working with specifications (MCP spec, A2A RFC docs)
- Ability to reason through distributed system edge cases
- Debugging distributed systems (logs, traces, replay)
- Collaborative debugging with other engineers
Preferred Qualifications
- Prior experience building custom protocol implementations
- Familiarity with message brokers (RabbitMQ, Kafka, MQTT)
- Experience with GraphQL or similar schema-driven APIs
- Understanding of OpenTelemetry or distributed tracing frameworks
- Exposure to microservices architectures
- Contributing to open-source protocol implementations
- Experience with Docker/containerization for protocol services
Technical Skills Breakdown
Skill DomainProficiency RequiredExamplesPython/JavaScriptExpertAsync/await, type hints, class hierarchies, decoratorsJSON-RPC 2.0AdvancedRequest/response handling, error codes, batch callsHTTP & RESTAdvancedClient libraries, headers, status codes, streamingAsync PatternsAdvancedEvent loops, futures/promises, async generatorsDistributed SystemsIntermediate-AdvancedLogging, tracing, eventual consistency, retriesServer ImplementationAdvancedCustom server architecture, lifecycle, concurrencyProtocol DesignIntermediate-AdvancedStateless vs. stateful, contract definition, versioningType Systems/SchemasIntermediateJSON Schema, TypeScript generics, runtime validationAgent ArchitecturesIntermediateTool calling, prompt engineering, orchestration patterns
Competency-Based Screening QuestionsTechnical Depth Questions
1. MCP Server Architecture
- "Walk us through how you would build an MCP server that exposes 5 different tools (web search, database query, email send, file upload, API call). How would you handle type safety, error recovery, and version compatibility?"
- Expected Answer: Should discuss JSON Schema for tool definitions, async handlers for each tool, error wrapping, client discovery mechanisms, graceful degradation if a tool fails.
2. A2A Agent Coordination
- "Design an A2A system where a Coordinator Agent needs to delegate tasks to 3 specialized agents (DataAgent, AnalysisAgent, ReportAgent). How would you implement task request routing, progress tracking, and failure handling?"
- Expected Answer: Should mention Agent Cards for capability advertisement, task payload structure, status tracking (queued → in-progress → complete), timeout management, fallback if an agent fails.
3. Protocol vs. Framework
- "What's the difference between building MCP from scratch using the mcp library vs. using a higher-level framework? When would you choose one over the other?"
- Expected Answer: Should understand that mcp library is low-level (JSON-RPC transport), and frameworks abstract this. Lower-level gives control but more work; frameworks are faster but less flexible.
4. Async & Concurrency
- "You have an MCP server that needs to handle 100 concurrent tool calls. How would you design concurrency limits, timeouts, and resource management?"
- Expected Answer: Should discuss async workers, queue management, semaphores, timeout contexts, backpressure handling, graceful degradation under load.
5. Distributed Tracing
- "How would you implement end-to-end tracing for an A2A request that spans 3 agents and 5 MCP tool calls? What would you log and how would you correlate logs?"
- Expected Answer: Should mention W3C Trace Context headers (trace-id, span-id), correlation IDs for tasks, structured logging (JSON with IDs), metrics instrumentation.
