Luis M. Gallardo D.

Luis M. Gallardo D.

MCP Servers Beyond Code: Research-Driven Development with Context7 and Terraform

6 minute read

MCP Servers for Research

Last week, I wanted to demonstrate the research capabilities of MCP servers, so I decided to analyze the AWS Backup Terraform module ecosystem. As the author of one of the modules in this space, I was curious: can MCP servers provide objective, comprehensive market analysis and competitive intelligence?

I used two specialized MCP servers for this research:

  • Context7: Retrieves up-to-date, version-specific documentation and code examples directly from libraries and frameworks, eliminating outdated or hallucinated information
  • Terraform MCP Server: Provides direct access to the Terraform Registry API, enabling comprehensive searches for modules, providers, and their metadata including download counts and version history

The result? A detailed analysis that showcased not just the modules’ capabilities, but the power of MCP servers for research-driven development. Here’s how these tools are revolutionizing the way we analyze and understand technology ecosystems.

🔍 The Research-First Approach

Traditional Workflow: Manual and Time-Consuming

Traditional Development Workflow

The traditional approach often leads to:

  • Manual browsing of https://registry.terraform.io/
  • Outdated documentation causing confusion
  • Limited information for decision-making
  • Costly refactoring when limitations are discovered

MCP Server Workflow: Automated and Comprehensive

MCP Server Development Workflow

The MCP server approach delivers:

  • Automated searches across the entire Registry
  • Up-to-date documentation for all candidates
  • Real adoption metrics and download counts
  • Comprehensive feature analysis before coding
  • Data-backed decisions with confidence

🎯 Real-World Case Study: AWS Backup Module Ecosystem Analysis

To test the research capabilities of MCP servers, I decided to analyze the AWS Backup Terraform module landscape. As the maintainer of the lgallard/backup/aws module, I wanted to see if MCP servers could provide objective market analysis and help understand the competitive positioning within this ecosystem.

The Research Query

Using Claude Code with connected MCP servers, I asked:

Prompt

The Analysis Process

The MCP servers immediately went to work:

  1. Terraform MCP Server: Searched the Terraform Registry for all AWS Backup modules
  2. Context7: Retrieved up-to-date documentation for each candidate
  3. Comparative Analysis: Generated feature matrices and maturity assessments
  4. Market Analysis: Provided objective positioning and competitive insights

The Results: Comprehensive Module Comparison

The analysis revealed five leading AWS Backup modules with detailed breakdown:

Module Downloads Version Key Features Maturity Score
cloudposse/backup/aws 1.8M 1.1.0 Advanced vault lock, Multiple backup rules, Cross-region copy, Comprehensive tagging, Audit framework support ⭐⭐⭐⭐⭐
lgallard/backup/aws 1.1M 1.0.2 Multiple plans support, Organizations policy, Audit framework, Reporting integration, Windows VSS backup ⭐⭐⭐⭐⭐
umotif-public/backup/aws 29K 1.6.0 SNS notifications, Basic vault management, Simple configuration, External vault support ⭐⭐⭐
boldlink/backup/aws 14K 1.0.1 IAM role management, Resource assignments, Modular vault support, Basic features only ⭐⭐⭐
USSBA/backup-plans/aws 10K 9.0.0 Cross-region backup, Government-focused, Simplified configuration, Limited customization ⭐⭐⭐

Detailed Feature Analysis

The MCP servers provided comprehensive capability breakdowns:

Research details

Why this module?

Why?

Market Analysis Results: Competitive Positioning

The research revealed interesting insights about the competitive landscape:

🏆 Market Leaders: cloudposse/backup/aws and lgallard/backup/aws

Key findings from the analysis:

  • Feature Parity: Both leading modules offer comprehensive enterprise features
  • Adoption Metrics: Similar download counts (1.8M vs 1.1M) indicate healthy competition
  • Differentiation: lgallard module excels in Organizations policy and Windows VSS support
  • Market Position: Clear separation between enterprise-grade modules and basic alternatives

Final recommendation

Final recommendation

🛠️ Setting Up MCP Servers for Research

Now that we’ve seen the power of MCP servers for competitive analysis and market research, let’s get them configured in your development environment. While I used Claude Code for this demonstration, MCP servers work with any AI tool that supports the Model Context Protocol—including Claude Desktop, Cursor, VS Code with Copilot, and other MCP-compatible applications. The setup process is straightforward, and once configured, these servers provide instant access to up-to-date documentation and registry data regardless of your chosen tool.

Context7 MCP Server Setup

For Claude Code:

# Install Context7 MCP server
npm install -g @upstash/context7-mcp

# Add to Claude Code MCP configuration
claude config mcp add context7 npm @upstash/context7-mcp

For Cursor:

// Add to Cursor's MCP settings
{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["@upstash/context7-mcp"]
    }
  }
}

Terraform MCP Server Setup

For Claude Code:

# Install Terraform MCP server
claude config mcp add terraform-server docker hashicorp/terraform-mcp-server

For Cursor:

// Add to Cursor's MCP configuration
{
  "mcpServers": {
    "terraform-server": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "hashicorp/terraform-mcp-server"]
    }
  }
}

Verification and Testing

MCP Server Connection

Once configured, verify your MCP servers are connected:

# In Claude Code
/mcp list

# In Cursor  
Check MCP status in settings > Tools > MCP Servers

📊 Research Workflow Best Practices

1. Start with Broad Discovery

Using Terraform MCP server, find all available modules for [technology/service]. 
Include download counts and latest versions.

2. Deep-Dive Documentation

Using Context7, compare the documentation quality and feature sets of the 
top 3 candidates. Focus on [specific requirements].

3. Generate Comparison Matrices

Create a detailed comparison table including features, maturity indicators, 
and suitability for [use case]. Provide a clear recommendation.

4. Validate with Current Information

The power of MCP servers lies in their real-time data access:

  • Context7 pulls the latest documentation versions
  • Terraform MCP accesses current registry statistics
  • No hallucinated or outdated information

🔬 Advanced Research Techniques

Multi-Criteria Decision Analysis

Ask your MCP-enabled AI assistant to:

Evaluate these modules using weighted criteria:
- Feature completeness (40%)
- Community adoption (25%)  
- Documentation quality (20%)
- Maintenance activity (15%)

Provide scores and final ranking.

Ecosystem Integration Research

Using Context7, research how [chosen module] integrates with:
- Existing Terraform providers
- CI/CD pipelines  
- Monitoring solutions
- Security frameworks

Cost and Performance Analysis

Using Terraform MCP server, analyze the resource efficiency and cost 
implications of each module approach.

🎯 When to Use MCP Servers for Research

Ideal Scenarios ✅

  • Competitive analysis - Understanding market positioning and feature gaps
  • Technology stack decisions - Comparing frameworks, libraries, or tools
  • Market research - Evaluating community adoption and trends
  • Architecture planning - Understanding integration patterns and limitations
  • Compliance research - Finding modules that meet specific regulatory requirements

Less Suitable Scenarios ⚠️

  • Simple, well-known implementations - Basic setups with obvious choices
  • Time-critical prototyping - When speed matters more than optimization
  • Internal tooling research - Limited public documentation availability

💡 Research ROI: Market Intelligence Benefits

The AWS Backup module ecosystem analysis took 20 minutes and delivered:

Market Intelligence:

  • Competitive positioning insights - Understanding where modules stand in the market
  • Feature gap analysis - Identifying opportunities for improvement
  • Adoption trend analysis - Real download metrics and community engagement

Research Quality Improvements:

  • Objective, data-driven analysis with standardized comparison criteria
  • Up-to-date information avoiding outdated documentation pitfalls
  • Comprehensive feature mapping across the entire ecosystem
  • Clear market positioning based on actual usage and features

🚀 The Future of Research-Driven Development

MCP servers represent a fundamental shift in how we approach technology research and competitive analysis. Instead of intuition-based or anecdotal insights, we can now:

  • Access real-time, accurate information from multiple sources simultaneously
  • Generate comprehensive market analyses with objective criteria
  • Conduct competitive intelligence backed by current adoption metrics
  • Identify market opportunities through thorough ecosystem analysis

The research phase isn’t just about gathering information—it’s about building comprehensive market understanding that informs product strategy, competitive positioning, and technology decisions.

🧠 Final thoughts

The future belongs to developers and teams who make informed decisions backed by real-time, accurate market data. MCP servers make this approach not just possible, but practical for everyday research and strategic work.

📚 References

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