MCP vs A2A vs Agent Evaluation: Protocols, Interoperability, and What to Measure

Three Layers, Three Concerns

The AI ecosystem is standardizing at three layers: context access (MCP), agent communication (A2A), and agent capability (evaluation). They're often conflated. Here's the mental model.

%%{init: {'theme': 'dark'}}%% graph TD classDef mcp fill:#1f3a93,stroke:#3498db,stroke-width:2px,color:#fff; classDef a2a fill:#6c3483,stroke:#9b59b6,stroke-width:2px,color:#fff; classDef agent fill:#1e824c,stroke:#2ecc71,stroke-width:2px,color:#fff; classDef mcpChild fill:#152968,stroke:#3498db,stroke-width:1px,color:#ecf0f1; classDef a2aChild fill:#4b2a5e,stroke:#9b59b6,stroke-width:1px,color:#ecf0f1; classDef agentChild fill:#105a32,stroke:#2ecc71,stroke-width:1px,color:#ecf0f1; Root["Evaluation Approaches"] style Root fill:#333,stroke:#666,stroke-width:2px,color:#fff MCP["MCP (Model Context Protocol)"] A2A["A2A (Agent-to-Agent)"] Agent["Agent Evaluation"] Root --> MCP Root --> A2A Root --> Agent class MCP mcp class A2A a2a class Agent agent %% MCP Dimensions MCP --> M1["Server Capability Discovery"] MCP --> M2["Tool Schema Validation"] MCP --> M3["Resource Access Control"] MCP --> M4["Context Injection Quality"] class M1,M2,M3,M4 mcpChild %% A2A Dimensions A2A --> A1["Agent Discovery"] A2A --> A2["Skill Negotiation"] A2A --> A3["Task Delegation Fidelity"] A2A --> A4["Inter-agent Communication"] A2A --> A5["Security Boundaries"] class A1,A2,A3,A4,A5 a2aChild %% Agent Evaluation Dimensions Agent --> E1["Autonomy Level"] Agent --> E2["Goal Achievement"] Agent --> E3["Tool Selection"] Agent --> E4["Planning Horizon"] Agent --> E5["Human-AI Collaboration"] class E1,E2,E3,E4,E5 agentChild

MCP: Standardizing Context Access

Model Context Protocol solves: "How does an LLM discover and use external tools, data, and prompts?" It's a client-server protocol where servers expose capabilities (tools, resources, prompts) and clients (LLMs/agents) consume them.

What MCP Standardizes

Capability discovery: list_tools(), list_resources(), list_prompts() — the client learns what's available at runtime
Tool invocation: JSON Schema-defined parameters, structured results, error handling
Resource access: URI-addressable data (files, DB rows, API responses) with MIME types
Prompt templates: Reusable, parameterized prompts stored server-side

MCP Evaluation Dimensions

Server capability discovery: Does the client correctly enumerate and understand available tools/resources? Test with schema evolution (added/removed/changed tools).
Tool schema validation: Does the client generate valid arguments per the JSON Schema? Measure: valid call rate, schema violation types, recovery from invalid calls.
Resource access control: Are permissions enforced? Can the client only access authorized resources? Test: RBAC, row-level security, token scoping.
Context injection quality: When resources are injected into the prompt, are they formatted correctly? Truncated appropriately? Relevant to the query?

A2A: Standardizing Agent Communication

Agent-to-Agent solves: "How do autonomous agents discover, negotiate, and delegate to each other?" It's a peer-to-peer protocol for agent interoperability.

What A2A Standardizes

Agent discovery: Advertising capabilities, skills, and endpoints (Agent Cards)
Task negotiation: Proposing, accepting, rejecting, or counter-offering tasks
Streaming execution: Real-time updates, partial results, clarification requests
Security: Authentication, authorization, audit trails for cross-agent calls

A2A Evaluation Dimensions

Agent discovery: Can agents find each other? Is the Agent Card accurate? Test: registry lookup, capability matching, version compatibility.
Skill negotiation: When Agent A delegates to Agent B, do they agree on the task schema, success criteria, and fallback? Measure: negotiation rounds, rejection rate, schema alignment.
Task delegation fidelity: Does the delegated task complete as specified? Compare: requested outcome vs delivered outcome. Metrics: completion rate, quality score, deviation from spec.
Inter-agent communication: Message format compliance, streaming chunk handling, timeout behavior, cancellation propagation.
Security boundaries: Can Agent A only invoke skills Agent B explicitly advertised? Are credentials scoped? Audit trail completeness.

Agent Evaluation: Measuring Capability, Not Protocol

While MCP and A2A are protocols, agent evaluation is a measurement discipline. It assesses: given tools (MCP) and peers (A2A), how well does the agent actually solve problems?

Core Evaluation Dimensions

Autonomy level: How much human intervention is needed? Scale: fully supervised → human-in-the-loop → human-on-the-loop → fully autonomous.
Goal achievement: Did the final state satisfy the user intent? Use LLM-as-judge with rubric + trajectory access.
Tool selection: Given N available tools (via MCP), does the agent pick the right one? Measure: precision@1, recall of relevant tools, hallucinated tool calls.
Planning horizon: How many steps ahead does the agent reason? Test with tasks requiring 1, 3, 5, 10+ steps. Measure: success rate vs horizon length.
Human-AI collaboration: When the agent escalates, is the context sufficient for the human to act? Measure: escalation rate, human time-to-resolution, context completeness.

How They Compose

┌─────────────────────────────────────────────────────────┐
│                    Agent Runtime                        │
├─────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐  │
│  │   Agent     │  │   Agent     │  │   Agent         │  │
│  │  (Planner)  │◄─┤  (Executor) │─►│  (Specialist)   │  │
│  └──────┬──────┘  └──────┬──────┘  └────────┬────────┘  │
│         │                │                   │           │
│         ▼                ▼                   ▼           │
│  ┌─────────────────────────────────────────────────┐   │
│  │              A2A Protocol Layer                  │   │
│  │  (Discovery • Negotiation • Delegation • Auth)   │   │
│  └─────────────────────────────────────────────────┘   │
│         │                │                   │           │
│         ▼                ▼                   ▼           │
│  ┌─────────────────────────────────────────────────┐   │
│  │              MCP Client Layer                    │   │
│  │  (Tool Calls • Resource Reads • Prompt Templates)│   │
│  └─────────────────────────────────────────────────┘   │
│         │                │                   │           │
│         ▼                ▼                   ▼           │
│  ┌─────────────────────────────────────────────────┐   │
│  │              MCP Servers                         │   │
│  │  (GitHub • Postgres • Slack • Filesystem • ...) │   │
│  └─────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘

Evaluation Strategy by Layer

Layer	Protocol	What to Evaluate	How
Context Access	MCP	Tool/schema compliance, resource permissions, context quality	Unit tests per server, integration tests for client, fuzzing schemas
Agent Communication	A2A	Discovery accuracy, delegation fidelity, security boundaries	Multi-agent simulation, contract testing, chaos injection
Agent Capability	N/A (discipline)	Goal achievement, planning, tool use, recovery	Trajectory eval, benchmark suites (AgentBench, ToolBench), production monitoring

Common Pitfalls

Evaluating the protocol, not the agent. Passing MCP schema validation ≠ the agent uses tools well.
Assuming A2A solves coordination. A2A standardizes the wire format; it doesn't guarantee agents will negotiate effectively. Test delegation scenarios explicitly.
Skipping MCP server eval. Your custom MCP servers need their own test suites: tool correctness, resource freshness, auth edge cases.
No cross-layer observability. Correlate: A2A delegation → MCP tool call → server response → agent decision. Without this, debugging multi-agent failures is guesswork.

Tooling Reality Check (Jan 2025)

MCP: Official SDKs (Python, TypeScript, Go), growing server ecosystem. Evaluation: mostly custom.
A2A: Early spec (Google-led), reference implementations emerging. Evaluation: nascent.
Agent eval: LangSmith, Weights & Biases, AgentBench, custom trajectory diffing.

Recommendation

Start with MCP server evaluation (unit test your tools). Add agent capability evaluation (trajectory + goal achievement). Layer A2A evaluation only when you have multiple agents that genuinely need to delegate — not for single-agent systems.

The protocol standards are enablers. The evaluation discipline is what makes the system reliable.