OpenCode Guide
Navigate: MCP Use Cases

Introduction

Getting Started

Guides & Integrations

Comparisons

[!TIP] 中文用户注意
如果您正在寻找模型上下文协议 (MCP) 的基础介绍或配置指南，可以先参考我们的中文文档：MCP 概览与配置指南。

OpenCode MCP Use Cases

MCP (Model Context Protocol) becomes powerful only when used in real workflows.
This page focuses on practical, production-oriented MCP use cases you can run inside OpenCode today — not abstract protocol explanations.
If you are new to MCP, start with the overview first:
👉 What is MCP in OpenCode?

What MCP Is Best At (Quick Summary)

MCP shines when tasks are:
  • Multi-step
  • Tool-heavy
  • Repetitive
  • Context-sensitive
  • Too complex for a single prompt
Typical MCP-powered workflows include:
  • Codebase-wide analysis
  • Automated refactors
  • Structured data access
  • Long-running development tasks
  • AI + tool orchestration

Use Case 1: Codebase Analysis & Architecture Review

Problem

You want an AI assistant to:
  • Understand a large codebase
  • Identify architecture issues
  • Suggest improvements consistently
Plain prompts fail because:
  • Context is too large
  • Analysis needs tooling (AST, file traversal)

MCP Solution

Use an MCP server that can:
  • Read files
  • Traverse directories
  • Parse code structure
  • Return structured summaries

Example Workflow

  1. OpenCode connects to a code-analysis MCP server
  2. MCP scans:
    • Project structure
    • Dependency graph
    • Entry points
  3. MCP returns structured context
  4. AI reasons on top of that context

Typical MCP Servers

  • Node.js-based AST analyzers
  • Language Server Protocol (LSP) adapters
  • Custom static analysis tools

Result

  • Faster onboarding
  • Consistent architecture reviews
  • Reliable high-level insights

Use Case 2: Large-Scale Refactoring Pipelines

Problem

You need to:
  • Rename APIs
  • Migrate patterns
  • Update imports
  • Enforce conventions
Across hundreds of files.
Manual work is slow and error-prone.

MCP Solution

MCP enables repeatable refactoring pipelines.

Example Workflow

  1. MCP server scans for target patterns
  2. MCP applies transformations
  3. MCP validates output
  4. OpenCode reviews or applies changes

Why MCP Works Well Here

  • Deterministic tools do the edits
  • AI focuses on decision-making
  • Changes remain auditable

Common Refactor Tasks

  • Framework migrations
  • Deprecated API removal
  • Monorepo restructuring
  • Style or lint normalization

Use Case 3: Data Access & Transformation

Problem

You want AI to:
  • Query databases
  • Read CSV / JSON / logs
  • Transform data
  • Generate reports
Without leaking credentials or embedding raw data into prompts.

MCP Solution

Use a data-access MCP server.

Example Workflow

  1. MCP connects to:
    • Databases
    • Local files
    • Data warehouses
  2. MCP executes queries
  3. MCP returns structured results
  4. AI interprets and summarizes

Typical MCP Servers

  • Python-based data processors
  • SQL query executors
  • Analytics pipelines

Benefits

  • Secure data handling
  • Structured results
  • Repeatable analysis

Use Case 4: External API Integration

Problem

You want AI to:
  • Fetch live data
  • Trigger actions
  • Integrate with third-party services
Without brittle prompt-based HTTP logic.

MCP Solution

Expose APIs via MCP servers.

Example Integrations

  • GitHub API (issues, PRs, commits)
  • CI/CD systems
  • Cloud providers
  • Internal services

Example Workflow

  1. MCP server wraps API logic
  2. AI calls MCP functions
  3. MCP handles authentication & retries
  4. AI reasons over responses

Result

  • Safer automation
  • Cleaner separation of concerns
  • Less prompt engineering

Use Case 5: Long-Running Development Context

Problem

Complex tasks span multiple sessions:
  • Feature development
  • Debugging investigations
  • Design iterations
AI loses context between runs.

MCP Solution

MCP servers can maintain long-lived state.

Example Workflow

  1. MCP stores:
    • Intermediate results
    • Decisions
    • Logs
  2. OpenCode reconnects later
  3. Context resumes seamlessly

Ideal For

  • Debug sessions
  • Incremental refactors
  • Research-heavy tasks

Use Case 6: Automation & Tool Orchestration

Problem

You need AI to coordinate multiple tools:
  • Linters
  • Test runners
  • Formatters
  • Build systems
In a controlled way.

MCP Solution

MCP acts as an orchestration layer.

Example Workflow

  1. MCP runs tests
  2. MCP analyzes failures
  3. MCP applies fixes
  4. MCP re-runs validation

Why MCP Beats Scripts

  • AI decides when and why
  • Tools execute how
  • Clear boundaries and logs

When You Should NOT Use MCP

MCP is not always necessary.
Avoid MCP when:
  • Task is simple or one-off
  • No external tools are needed
  • Context fits comfortably in prompts
MCP introduces structure — use it when structure pays off.

Choosing the Right MCP Server Type

| Use Case | Recommended Server | |--------|--------------------| | Code analysis | Node.js / LSP | | Refactoring | AST-based tools | | Data processing | Python | | API integration | Language-native SDKs | | Automation | Task runners |

How These Use Cases Fit Into OpenCode

OpenCode acts as:
  • The orchestrator
  • The UX layer
  • The reasoning engine
MCP servers act as:
  • Tool providers
  • Context managers
  • Execution layers
Together, they enable workflows that plain chat-based coding tools cannot handle.

Next Steps

This page will evolve as new MCP servers and OpenCode workflows emerge.