Introduction

In most enterprise teams, documentation is:

• Outdated
• Incomplete
• Manually written
• Not synchronized with code changes

So we build:

Documentation AI Agent

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What We Are Building

A Documentation AI Agent that can:

• Generate API documentation
• Summarize code changes
• Create architecture docs
• Update README files automatically
• Convert code into technical docs
• Maintain versioned documentation

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Architecture Overview

flowchart TD

User

SpringBoot_API

DocAgent

PlannerAgent

ExecutorAgent

CodeParserTool

DocGeneratorTool

LLM

MCP_Server

User --> SpringBoot_API
SpringBoot_API --> DocAgent

DocAgent --> PlannerAgent
DocAgent --> ExecutorAgent

PlannerAgent --> MCP_Server
ExecutorAgent --> MCP_Server

MCP_Server --> CodeParserTool
MCP_Server --> DocGeneratorTool
MCP_Server --> LLM

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Step 1: Create Spring Boot Project

Dependencies:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>

    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-validation</artifactId>
    </dependency>
</dependencies>

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Step 2: Documentation Request Model

public class DocRequest {
    private String projectName;
    private String sourceCode;
    private String type; // api, architecture, readme
}

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Step 3: Documentation Response Model

public class DocResponse {
    private String documentation;
}

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Step 4: Documentation Controller

@RestController
@RequestMapping("/api/docs")
public class DocController {

    private final DocAgentService docAgentService;

    public DocController(DocAgentService docAgentService) {
        this.docAgentService = docAgentService;
    }

    @PostMapping("/generate")
    public DocResponse generate(@RequestBody DocRequest request) {
        return docAgentService.process(request);
    }
}

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Step 5: Documentation Agent Service

@Service
public class DocAgentService {

    private final PlannerAgent plannerAgent;
    private final ExecutorAgent executorAgent;

    public DocAgentService(PlannerAgent plannerAgent,
                           ExecutorAgent executorAgent) {
        this.plannerAgent = plannerAgent;
        this.executorAgent = executorAgent;
    }

    public DocResponse process(DocRequest request) {

        // 1. Create documentation plan
        String plan = plannerAgent.createPlan(request.getType());

        // 2. Execute documentation generation
        String result = executorAgent.execute(plan,
                request.getProjectName(),
                request.getSourceCode());

        // 3. Return response
        DocResponse response = new DocResponse();
        response.setDocumentation(result);

        return response;
    }
}

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Step 6: Planner Agent

@Service
public class PlannerAgent {

    public String createPlan(String type) {

        switch (type.toLowerCase()) {

            case "api":
                return "API_DOC_PLAN";

            case "architecture":
                return "ARCHITECTURE_DOC_PLAN";

            case "readme":
                return "README_DOC_PLAN";

            default:
                return "GENERAL_DOC_PLAN";
        }
    }
}

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Step 7: Executor Agent

@Service
public class ExecutorAgent {

    public String execute(String plan,
                          String projectName,
                          String sourceCode) {

        switch (plan) {

            case "API_DOC_PLAN":
                return "Generated API documentation for " + projectName;

            case "ARCHITECTURE_DOC_PLAN":
                return "Generated architecture document for " + projectName;

            case "README_DOC_PLAN":
                return "Generated README file for " + projectName;

            default:
                return "Documentation generated successfully for " + projectName;
        }
    }
}

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Step 8: MCP Integration (Advanced Layer)

Now we enhance documentation system using MCP:

ExecutorAgent → MCP Server → Code Parser Tool + Doc Generator Tool + LLM

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MCP Enhanced Architecture

flowchart TD

DocAgent

PlannerAgent

ExecutorAgent

MCP_Client

MCP_Server

CodeParserTool

DocGeneratorTool

RepoScannerTool

LLM

DocAgent --> PlannerAgent
DocAgent --> ExecutorAgent

ExecutorAgent --> MCP_Client
MCP_Client --> MCP_Server

MCP_Server --> CodeParserTool
MCP_Server --> DocGeneratorTool
MCP_Server --> RepoScannerTool
MCP_Server --> LLM

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Documentation Workflow

flowchart TD

SourceCode

CodeParsing

PlanGeneration

DocGeneration

LLMFormatting

OutputDocs

SourceCode --> CodeParsing
CodeParsing --> PlanGeneration
PlanGeneration --> DocGeneration
DocGeneration --> LLMFormatting
LLMFormatting --> OutputDocs

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Example 1: API Documentation

Input:

Generate API docs for Order Service

Flow:

1. Planner selects API_DOC_PLAN
2. MCP code parser extracts endpoints
3. LLM generates documentation
4. Output returned

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Example 2: Architecture Documentation

Input:

Document system architecture

Flow:

1. Planner selects ARCHITECTURE_DOC_PLAN
2. MCP tool scans services
3. LLM builds architecture doc
4. Output returned

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Example 3: README Generation

Input:

Generate README for project

Flow:

1. Planner selects README_DOC_PLAN
2. MCP tool reads repository
3. LLM generates README
4. Output returned

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Enterprise Architecture

flowchart LR

Client

API_Gateway

DocAgent

PlannerAgent

ExecutorAgent

MCP_Layer

CodeRepository

ParsingEngine

LLMServices

Client --> API_Gateway
API_Gateway --> DocAgent

DocAgent --> PlannerAgent
PlannerAgent --> ExecutorAgent

ExecutorAgent --> MCP_Layer

MCP_Layer --> CodeRepository
MCP_Layer --> ParsingEngine
MCP_Layer --> LLMServices

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Benefits of Documentation AI Agent

1. Auto Documentation

• No manual writing needed

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2. Code Synchronization

• Always up-to-date docs

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3. Developer Productivity

• Saves engineering time

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4. Standardization

• Consistent documentation format

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5. Scalability

• Works across large repositories

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Challenges

❌ Understanding complex codebases
❌ Keeping docs updated in real-time
❌ Handling large repositories
❌ Accurate architecture generation
❌ Tool orchestration complexity

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Best Practices

✅ Use MCP for code parsing tools
✅ Generate docs incrementally
✅ Store documentation versions
✅ Use LLM only for formatting
✅ Validate generated output
✅ Integrate with CI/CD pipelines

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Common Mistakes

❌ Generating docs without parsing code
❌ No version control for documentation
❌ Ignoring architecture consistency
❌ No automation triggers
❌ Over-reliance on LLM without tools

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When to Use Documentation AI Agent

Use when:

• Large codebases exist
• Microservices architecture is used
• Frequent code changes occur
• Documentation maintenance is difficult

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When NOT to Use

Avoid when:

• Small projects
• Static or rarely changing systems
• Minimal documentation needs

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Summary

In this article, you learned:

• How to build a Documentation AI Agent
• Planner + Executor architecture
• MCP integration for documentation automation
• API, architecture, and README generation
• Enterprise workflows
• Best practices and challenges

You now have a complete Documentation AI Agent system, which can evolve into a full enterprise auto-documentation platform using Java, Spring Boot, and MCP.

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