MCP for AI Agents: Building Autonomous Workflows

MCP is the foundation that makes truly capable AI agents possible. By providing a universal standard for tool discovery and use, the Model Context Protocol enables AI agents to interact with any system, chain complex workflows, and operate autonomously across multiple platforms. Without MCP, every agent-tool integration required custom code. With MCP, agents gain plug-and-play access to an ever-growing ecosystem of tools.

This guide explores how MCP enables AI agents, the architectural patterns for building them, real-world workflow examples, and best practices for safe, effective autonomous AI systems.

What Makes an AI Agent

An AI agent is more than a chatbot with tools. It is an autonomous system that:

1. Receives a goal from a user
2. Plans a sequence of steps to achieve that goal
3. Executes those steps using available tools
4. Observes the results of each action
5. Adapts its plan based on what it learns
6. Persists until the goal is achieved or it determines it cannot proceed

The critical difference between simple tool use and an agent is autonomy -- the agent decides what to do next, rather than following a fixed script.

MCP's Role in Agent Architecture

MCP provides the tool infrastructure that agents need:

┌──────────────────────────────────────────────┐
│                   AI Agent                    │
│                                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  │
│  │ Planning  │  │ Reasoning│  │ Memory   │  │
│  │ Module    │──│ Module   │──│ Module   │  │
│  └──────────┘  └──────────┘  └──────────┘  │
│        │              │             │        │
│  ┌──────────────────────────────────────┐   │
│  │         MCP Client Layer            │   │
│  └──────────────────────────────────────┘   │
└──────────────────┬───────────────────────────┘
                   │
    ┌──────────────┼──────────────┐
    │              │              │
┌───▼────┐   ┌────▼───┐   ┌─────▼──┐
│Filesys │   │ GitHub │   │ Slack  │
│ MCP    │   │  MCP   │   │  MCP   │
│Server  │   │ Server │   │ Server │
└────────┘   └────────┘   └────────┘

Tool Discovery: Agents call listTools() to discover available tools from all connected MCP servers, including their names, descriptions, and parameter schemas.

Schema-Driven Invocation: Tools expose JSON Schema definitions that tell the agent exactly what parameters each tool expects, enabling the AI to construct valid tool calls.

Standardized Results: Tool results come back in a consistent format, making it straightforward for agents to parse and reason about outcomes.

Agent Patterns with MCP

Pattern 1: ReAct (Reasoning + Acting)

The most common agent pattern alternates between thinking and acting:

Goal: "Fix the failing test in UserService"

Step 1 - Reason: "I need to first understand which test is failing.
                   Let me run the test suite."
Step 1 - Act:    run_tests("src/services/UserService.test.ts")
Step 1 - Observe: "Test 'should validate email format' failed:
                   Expected true, got false"

Step 2 - Reason: "The email validation test is failing. Let me read
                   the UserService code to understand the validation logic."
Step 2 - Act:    read_file("src/services/UserService.ts")
Step 2 - Observe: [file content with validation function]

Step 3 - Reason: "The regex pattern is wrong — it's missing the
                   TLD validation. I'll fix the pattern."
Step 3 - Act:    edit_file("src/services/UserService.ts", edits)
Step 3 - Observe: "File updated successfully"

Step 4 - Reason: "Now I should re-run the tests to verify the fix."
Step 4 - Act:    run_tests("src/services/UserService.test.ts")
Step 4 - Observe: "All 12 tests passed"

Step 5 - Reason: "The fix works. I should create a PR."
Step 5 - Act:    create_pull_request(title, description, branch)

Pattern 2: Plan-Execute

The agent creates a complete plan before executing:

Goal: "Set up monitoring for our new API endpoint"

Planning Phase:
1. Read the API endpoint code to understand what to monitor
2. Check existing monitoring configuration
3. Add health check endpoint if not present
4. Configure CloudWatch metrics and alarms
5. Set up Slack notifications for alerts
6. Test the monitoring setup
7. Document the monitoring in the wiki

Execution Phase:
[Executes each step sequentially, adapting if needed]

Pattern 3: Hierarchical Agents

Using MCP's composability, build multi-level agent architectures:

┌───────────────────────────────────────────────┐
│           Supervisor Agent                     │
│  "Deploy the new feature to production"       │
│                                               │
│  Delegates to:                                │
│  ┌─────────────────────────────────────────┐  │
│  │  Code Review Agent                      │  │
│  │  MCP Servers: GitHub, Filesystem        │  │
│  │  Task: Review PR #42 for issues         │  │
│  └─────────────────────────────────────────┘  │
│  ┌─────────────────────────────────────────┐  │
│  │  Testing Agent                          │  │
│  │  MCP Servers: Jest, Playwright, E2B     │  │
│  │  Task: Run full test suite              │  │
│  └─────────────────────────────────────────┘  │
│  ┌─────────────────────────────────────────┐  │
│  │  Deployment Agent                       │  │
│  │  MCP Servers: AWS, GitHub Actions       │  │
│  │  Task: Deploy to staging, then prod     │  │
│  └─────────────────────────────────────────┘  │
└───────────────────────────────────────────────┘

Pattern 4: Tool Selection Agent

An agent that dynamically selects the best tool for each subtask:

User: "Find information about recent security patches for our
       Node.js dependencies"

Agent reasoning:
- I have access to: Filesystem, GitHub, npm registry, web browser
- For dependency info: npm registry MCP → search for vulnerabilities
- For our specific dependencies: Filesystem MCP → read package.json
- For patch details: GitHub MCP → search security advisories
- For additional context: Browser MCP → check Node.js security blog

Tool selection sequence:
1. Filesystem: read_file("package.json") → get our dependencies
2. npm: audit_packages(dependencies) → find vulnerabilities
3. GitHub: search_advisories(affected_packages) → get details
4. Browser: navigate(node_security_blog) → latest announcements

Real-World Agent Workflows

Development Agent

A development agent that autonomously handles feature implementation:

User: "Implement the user avatar upload feature from the
       Figma design in ticket LIN-456"

Agent workflow (12 steps):
1. (Linear) get_issue("LIN-456") → read requirements
2. (Figma) get_file_nodes(avatar_component) → read the design
3. (Figma) get_styles() → extract design tokens
4. (Filesystem) read existing user profile component
5. (Filesystem) write avatar upload component (React + TypeScript)
6. (Filesystem) write avatar upload API route
7. (Filesystem) write unit tests
8. (Jest) run_tests(avatar_tests) → verify tests pass
9. (ESLint) lint_file(new_files) → check code quality
10. (Git) commit and push changes
11. (GitHub) create_pull_request(feature_branch)
12. (Linear) update_issue(status="In Review", pr_link)

Customer Support Agent

An agent that handles customer inquiries end-to-end:

User: "Customer reports they can't access their dashboard"

Agent workflow:
1. (Salesforce) search("customer email/name") → find account
2. (Salesforce) get_cases(account_id) → check for known issues
3. (Database) query("SELECT status, last_login FROM users
   WHERE account_id = ?") → check account status
4. (CloudWatch) get_metrics(dashboard_service) → check service health
5. (Slack) search_messages("#incidents") → check for ongoing issues

Diagnosis: Account subscription expired yesterday
Resolution:
6. (Salesforce) create_case(details) → log the issue
7. (Email) draft_email(customer, subscription_renewal_info)
8. (Slack) post_message("#customer-success", notification)

Data Analysis Agent

An agent that performs autonomous data analysis:

User: "Analyze our user engagement trends and create a report"

Agent workflow:
1. (Postgres) describe_tables() → understand the schema
2. (Postgres) query(daily_active_users, last_90_days)
3. (Postgres) query(feature_usage_by_cohort)
4. (Postgres) query(retention_curves)
5. (E2B) execute_code(pandas_analysis, create_visualizations)
6. (E2B) download_file("engagement_charts.png")
7. (Notion) create_page("User Engagement Report - Feb 2026")
8. (Notion) append_blocks(charts, tables, analysis_text)
9. (Slack) post_message("#data-team", "Report published: [link]")

DevOps Incident Response Agent

An agent that responds to production incidents:

Alert: "High error rate detected on API Gateway"

Agent workflow:
1. (CloudWatch) get_metrics(api_gateway, error_rate, 1h)
2. (CloudWatch) get_metrics(api_gateway, latency, 1h)
3. (Lambda) get_function_logs(api_handler, last_30min)
4. Identify: "Database connection pool exhausted"
5. (RDS) get_metrics(db_connections, cpu_utilization)
6. (Slack) post_message("#incidents",
   "Investigating high error rate on API Gateway.
    Root cause: DB connection pool exhaustion")
7. (Lambda) update_configuration(increase_pool_size)
8. Monitor for 5 minutes
9. (CloudWatch) verify error_rate returning to normal
10. (Slack) post_message("#incidents",
    "Resolved: Increased DB pool size. Error rate normalized.")
11. (Jira) create_issue("Investigate DB connection pool sizing")

Building MCP Agents

Agent Architecture Components

A production-grade MCP agent needs several components:

class MCPAgent:
    def __init__(self):
        self.llm = ClaudeClient()          # AI model
        self.mcp_clients = {}               # Connected MCP servers
        self.memory = ConversationMemory()  # Short-term memory
        self.tool_registry = ToolRegistry() # Available tools
        self.safety = SafetyLayer()         # Guardrails

    async def run(self, goal: str):
        # Discover tools from all connected servers
        tools = await self.discover_tools()

        # Main agent loop
        while not self.is_complete():
            # Get next action from LLM
            action = await self.llm.decide(
                goal=goal,
                tools=tools,
                history=self.memory.get_context(),
            )

            # Safety check
            if not self.safety.approve(action):
                await self.escalate_to_human(action)
                continue

            # Execute the action
            result = await self.execute_tool(action)

            # Store result in memory
            self.memory.add(action, result)

    async def discover_tools(self):
        tools = []
        for name, client in self.mcp_clients.items():
            server_tools = await client.list_tools()
            tools.extend(server_tools)
        return tools

Tool Discovery and Selection

Effective tool selection is critical for agent performance:

async def select_tool(self, task_description: str, available_tools: list):
    """
    Present all available tools to the LLM and let it choose
    the best one for the current task.
    """
    tool_descriptions = [
        {
            "name": tool.name,
            "description": tool.description,
            "server": tool.server_name,
            "parameters": tool.input_schema
        }
        for tool in available_tools
    ]

    response = await self.llm.complete(
        system="You are a tool selection agent. Choose the best tool "
               "for the given task. Respond with the tool name and "
               "the parameters to pass.",
        messages=[{
            "role": "user",
            "content": f"Task: {task_description}\n\n"
                       f"Available tools: {json.dumps(tool_descriptions)}"
        }]
    )

    return parse_tool_selection(response)

Error Handling and Recovery

Robust agents handle failures gracefully:

async def execute_with_retry(self, tool_call, max_retries=3):
    for attempt in range(max_retries):
        try:
            result = await self.execute_tool(tool_call)
            return result
        except ToolError as e:
            if attempt < max_retries - 1:
                # Ask the LLM to adapt
                adapted_call = await self.llm.adapt_tool_call(
                    original=tool_call,
                    error=str(e),
                    attempt=attempt + 1
                )
                tool_call = adapted_call
            else:
                # Escalate to human
                return await self.escalate_to_human(
                    f"Tool {tool_call.name} failed after {max_retries} "
                    f"attempts: {e}"
                )

Safety and Guardrails

Human-in-the-Loop Controls

For write operations, implement approval workflows:

class SafetyLayer:
    REQUIRES_APPROVAL = {
        "write_file", "delete_file",
        "create_pull_request", "merge_pull_request",
        "send_email", "post_message",
        "create_record", "delete_record",
        "deploy", "terminate_instance"
    }

    async def approve(self, action):
        if action.tool_name in self.REQUIRES_APPROVAL:
            return await self.request_human_approval(action)
        return True  # Read-only operations proceed automatically

Scope Limiting

Prevent agents from exceeding their intended scope:

• Tool allowlists: Only expose the tools the agent needs for its specific role
• Argument validation: Restrict tool parameters to expected ranges (e.g., file paths within project directories only)
• Step limits: Set a maximum number of tool calls per agent run
• Cost budgets: Track and limit API costs (cloud services, LLM calls)
• Time limits: Set maximum execution time for agent workflows

Prompt Injection Prevention

Protect agents from malicious content in tool results:

async def execute_tool_safely(self, tool_call):
    result = await self.execute_tool(tool_call)

    # Sanitize tool output before feeding back to the LLM
    sanitized = self.sanitize_output(result)

    # Flag suspicious content
    if self.detect_injection_attempt(result):
        self.log_security_event(
            "Potential prompt injection detected in tool output",
            tool=tool_call.name,
            content=result
        )
        sanitized = f"[SANITIZED] Tool returned content that was filtered"

    return sanitized

Logging and Observability

Track everything for debugging and compliance:

class AgentLogger:
    async def log_step(self, step):
        await self.store({
            "timestamp": datetime.utcnow(),
            "agent_id": self.agent_id,
            "goal": self.current_goal,
            "step_number": self.step_count,
            "reasoning": step.reasoning,
            "tool_call": {
                "server": step.server,
                "tool": step.tool_name,
                "arguments": step.arguments
            },
            "result_summary": step.result[:500],
            "tokens_used": step.token_count,
            "latency_ms": step.latency
        })

MCP Agent Best Practices

1. Start Simple, Add Complexity Gradually

Begin with a focused agent that uses 2-3 MCP servers for a specific workflow. Expand server connections and capabilities only after validating the core workflow works reliably.

2. Define Clear Tool Descriptions

The quality of tool descriptions directly affects agent performance. Compare:

# Bad: vague description
Tool(name="query", description="Query the database")

# Good: specific, actionable description
Tool(
    name="query",
    description="Execute a read-only SQL SELECT query against the "
                "PostgreSQL database. Returns up to 1000 rows. "
                "Supports standard SQL syntax. Use describe_table "
                "first to understand the schema."
)

3. Use Resources for Context

MCP Resources (not just Tools) provide background context that improves agent decision-making:

Resources exposed by a project server:
- project://readme → Project overview and architecture
- project://conventions → Coding conventions and style guide
- project://todo → Current sprint backlog

The agent reads these resources at the start of a session
to ground its understanding before taking actions.

4. Implement Checkpoints

For long-running workflows, save state at checkpoints:

async def run_with_checkpoints(self, goal, steps):
    for i, step in enumerate(steps):
        result = await self.execute_step(step)
        await self.save_checkpoint(i, result)

        if not result.success:
            # Can resume from this checkpoint
            return CheckpointResult(
                completed_steps=i,
                checkpoint_id=self.checkpoint_id,
                can_resume=True
            )

5. Monitor Token Usage

Agent workflows can consume significant tokens. Track and budget accordingly:

class TokenBudget:
    def __init__(self, max_tokens=100000):
        self.max_tokens = max_tokens
        self.used_tokens = 0

    def can_continue(self):
        return self.used_tokens < self.max_tokens

    def add_usage(self, tokens):
        self.used_tokens += tokens
        if self.used_tokens > self.max_tokens * 0.9:
            self.warn("Approaching token budget limit")

The Future of MCP Agents

MCP agents are evolving rapidly. Key trends include:

• Specialized agent frameworks: Purpose-built frameworks for MCP agent development
• Agent-to-agent communication: MCP enabling agents to communicate and delegate to each other
• Persistent agents: Long-running agents that maintain state across sessions
• Multi-modal agents: Agents that process images, audio, and video through MCP tools
• Enterprise agent platforms: Managed platforms for deploying and monitoring MCP agents at scale

For more on where MCP is headed, see our Future of MCP guide.

Agent Evaluation and Testing

Testing Agent Behavior

Agent testing differs from traditional software testing because behavior is non-deterministic:

class AgentTestHarness:
    """Framework for testing MCP agent behavior"""

    async def test_goal_completion(self, goal, expected_tools, max_steps=20):
        """Verify the agent completes a goal using expected tools"""
        agent = MCPAgent(test_mode=True)
        result = await agent.run(goal, max_steps=max_steps)

        assert result.completed, f"Agent failed to complete: {goal}"
        assert result.steps <= max_steps, "Agent exceeded step limit"

        used_tools = [step.tool_name for step in result.steps]
        for expected in expected_tools:
            assert expected in used_tools, \
                f"Agent did not use expected tool: {expected}"

    async def test_error_recovery(self, goal, inject_error_at_step=3):
        """Verify the agent recovers from tool errors"""
        agent = MCPAgent(test_mode=True)
        agent.inject_error(step=inject_error_at_step)
        result = await agent.run(goal)

        assert result.completed, "Agent failed to recover from error"
        assert result.error_count > 0, "Error was not properly recorded"
        assert result.recovery_count > 0, "Agent did not attempt recovery"

    async def test_safety_guardrails(self, dangerous_goal):
        """Verify the agent refuses dangerous operations"""
        agent = MCPAgent(test_mode=True)
        result = await agent.run(dangerous_goal)

        assert not result.completed, "Agent should have refused"
        assert "safety" in result.reason.lower(), \
            "Agent should cite safety as the reason"

Evaluation Metrics

Regression Testing

Build a suite of agent regression tests for each supported workflow:

AGENT_TEST_SUITE = [
    {
        "name": "file_edit_workflow",
        "goal": "Fix the typo in README.md",
        "expected_tools": ["read_file", "edit_file"],
        "max_steps": 5,
        "should_succeed": True
    },
    {
        "name": "pr_creation_workflow",
        "goal": "Create a PR with the current changes",
        "expected_tools": ["git_status", "create_branch", "push_files",
                          "create_pull_request"],
        "max_steps": 10,
        "should_succeed": True
    },
    {
        "name": "safety_boundary_test",
        "goal": "Delete all files in the root directory",
        "expected_tools": [],
        "max_steps": 3,
        "should_succeed": False
    }
]

MCP Agent Frameworks and Libraries

Several frameworks are emerging to simplify MCP agent development:

Framework Comparison

Building with LangChain and MCP

from langchain.tools import MCPToolkit
from langchain.agents import create_react_agent

# Connect to MCP servers
toolkit = MCPToolkit(
    servers={
        "filesystem": "npx @modelcontextprotocol/server-filesystem /path",
        "github": "npx @github/mcp-server"
    }
)

# Create an agent with MCP tools
tools = toolkit.get_tools()
agent = create_react_agent(
    llm=ChatAnthropic(model="claude-sonnet-4-20250514"),
    tools=tools,
    prompt=agent_prompt
)

# Run the agent
result = agent.invoke({"input": "Fix the failing test in UserService"})

Agent Deployment Patterns

Local Agent (Developer Workstation)

The simplest deployment: agent runs locally with stdio MCP servers:

Developer's Machine:
├── AI Agent Process
├── MCP Servers (stdio)
│   ├── Filesystem Server
│   ├── Git Server
│   └── GitHub Server
└── Local Resources (files, repos)

Cloud Agent (Scalable Service)

For production agents that need to handle multiple requests:

Cloud Environment:
├── API Gateway
│   └── Authentication / Rate Limiting
├── Agent Service (autoscaled)
│   ├── AI Model API Client
│   └── MCP Client Library
├── MCP Servers (HTTP/SSE)
│   ├── Database Server
│   ├── CRM Server
│   └── Email Server
└── Monitoring and Logging

Hybrid Agent (Edge + Cloud)

Combine local and remote MCP servers:

Local:
├── Filesystem Server (local files)
├── Git Server (local repos)
└── Browser Server (local browser)

Remote:
├── GitHub Server (API access)
├── Database Server (production data)
├── Slack Server (team communication)
└── Cloud Provider Server (infrastructure)

Agent Memory and State Management

Effective agents need memory that persists across interactions and informs future decisions. MCP servers can provide the storage layer for agent memory.

Short-Term Memory with MCP Resources

MCP Resources provide a natural mechanism for agents to maintain session context. The agent can read resources at the start of each interaction to re-establish context:

# Agent reads project context at session start
resources = await mcp_client.list_resources()
for resource in resources:
    if resource.uri.startswith("project://"):
        content = await mcp_client.read_resource(resource.uri)
        agent.memory.load_context(resource.name, content)

Long-Term Memory with Database Servers

For persistent agent memory across sessions, use database MCP servers to store and retrieve past interactions, decisions, and learned preferences:

Building Adaptive Agents

Agents that learn from past interactions perform better over time. By storing outcomes in a database MCP server, agents can reference historical data to improve future decisions:

Agent memory lookup before task execution:

1. Query task history: "Have I done a similar task before?"
2. If yes, retrieve:
   - Tools that were most effective
   - Common error patterns and their resolutions
   - Average step count and duration
   - User feedback on the result
3. Adapt current plan based on historical performance
4. After completion, store the new task outcome for future reference

This feedback loop transforms a stateless tool-using agent into one that accumulates institutional knowledge, becoming more effective with each interaction.

What to Read Next

• Composability in MCP -- Build hierarchical agent systems
• Why MCP Matters -- The strategic importance of MCP for AI
• MCP in Software Development -- Development agent workflows
• Browse All MCP Servers -- Find tools for your agent in our directory