Introduction

Software architects work in complex ecosystems every day, switching between AWS consoles, GitHub repos, Grafana dashboards, Kanban boards, documentation, and architectural diagrams. Using MCP servers can significantly boost productivity by letting architects pull all of this into an AI assistant like Claude. This means less time searching for answers and more time for critical decisions. In this post, we’ll share the top 10 MCP tools that software architects use to get that productivity boost!

What is MCP?

“Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.” Model Context Protocol README

An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. AI tools like Claude Desktop, Cursor, Windsurf, and Copilot connect to these servers to access local data sources and remote services, providing additional context that improves their outputs. As a user, you can integrate third-party tools through MCP to give your AI assistant direct access to your systems, allowing you to query them simply by asking questions.

Here are the top MCP tools software architects need for their daily workflows. 👇

1. AWS MCP

The AWS MCP server gives architects direct access to their AWS infrastructure through natural language queries. You can inspect resources across services like EC2, S3, Lambda, RDS, and more using your AI assistant. Architects can ask questions like “show me all Lambda functions in us-east-1 with high memory usage” or “list RDS instances and their backup configurations”. This is valuable when designing system architectures, troubleshooting production issues, or reviewing resource configurations.

2. GitHub MCP

https://github.com/github/github-mcp-server

The GitHub MCP server enables architects to interact with repositories, issues, pull requests, and workflows directly through their AI assistant. This is essential for reviewing code architecture, understanding repository structures, and analysing PR discussions. Architects can ask questions like “what are the open architectural decision records?” or “show me the recent changes to the authentication module” without manually searching through GitHub’s interface.

GitLab Alternative: If you’re working with GitLab, they also have an official MCP server:
https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/

3. Incident.io MCP

https://incident.io/changelog/introducing-incident-io-mcp

For architects responsible for system reliability and incident response, the Incident.io MCP server provides crucial access to incident history, postmortems, and ongoing incidents. Having an AI assistant that helps with incident management is valuable for analysing patterns and making data-driven decisions for system resilience.

4. Atlassian MCP

https://www.atlassian.com/platform/remote-mcp-server

The Atlassian MCP server connects to Jira, Confluence, and other Atlassian products, bringing project management and documentation directly into your AI workflow. Architects can query sprint status, retrieve architectural documentation from Confluence, and understand team capacity without switching contexts. This is particularly powerful for maintaining architecture decision records (ADRs), understanding current project priorities, and aligning technical decisions with business requirements.

5. IcePanel MCP

https://icepanel.io/blog/2025-05-15-new-mcp-server

IcePanel’s MCP server brings visual architecture diagrams and models directly into your AI workflow. IcePanel specialises in C4 model diagrams and system architecture visualisation, allowing architects to query their architecture models, understand component relationships, and keep diagrams in sync with code and infrastructure. You can ask questions like “show me all services that depend on this API”, “what does team X own in this system?”, or “what is the most commonly used technology in my system?”
This tool is particularly valuable for maintaining architectural documentation, onboarding new team members, and communicating system design to different stakeholders.

6. Honeycomb MCP

https://github.com/honeycombio/honeycomb-mcp

Honeycomb’s MCP server enables architects to leverage observability data for deep system understanding. With this MCP server, architects can query traces, analyze service dependencies, and investigate performance issues through natural language.

7. Terraform MCP

https://github.com/hashicorp/terraform-mcp-server

The Terraform MCP server is a game-changer for infrastructure-as-code workflows. Architects can query Terraform state, understand resource dependencies, review planned changes, and even generate Terraform configurations through AI assistance. This accelerates infrastructure design by allowing questions like “what resources depend on this VPC?” or “show me the current configuration of our production Redis cluster”.

8. MCP Toolbox for Databases

https://github.com/googleapis/genai-toolbox

This open-source MCP server specialises in database operations and supports a comprehensive range of database systems including AlloyDB, BigQuery, Bigtable, Cloud SQL, Dgraph, Looker, MySQL, Neo4j, Postgres, Spanner, and more. For architects designing data-intensive systems, this tool enables querying schema information, analysing query patterns, and understanding data relationships across multiple database types.

9. Grafana MCP

https://github.com/grafana/mcp-grafana

The Grafana MCP server brings observability and monitoring data into your AI assistant, allowing architects to query metrics, dashboards, and alerts in natural language. Instead of building complex Grafana queries or navigating multiple dashboards, you can simply ask “show me API latency trends over the last week” or “what alerts fired during the last deployment?” This accelerates performance analysis and helps architects quickly understand system behavior during incidents or deployments.

10. Notion MCP

https://github.com/makenotion/notion-mcp-server#readme

Many teams use Notion for architecture documentation, design proposals, meeting notes, and project management. The Notion MCP server allows architects to quickly retrieve and reference documentation, search across team knowledge, and update pages through AI assistance. Instead of searching through nested Notion pages, you can ask “what did we decide about the database scaling approach?” or “show me the API design guidelines”. This means architectural context is always accessible when making decisions.

Shortcut Alternative: For teams using Shortcut for project management, there’s also an MCP server: https://github.com/useshortcut/mcp-server-shortcut

Conclusion

Having MCP servers enables architects to bring their entire ecosystem (cloud infrastructure, code repositories, monitoring systems, documentation, and project management) into a unified conversational interface. This is a game changer for software architects. This means faster decision-making, less context-switching, and an enhanced ability to analyse complex systems holistically.

The MCP ecosystem is growing rapidly, with new integrations being released regularly. There are many more worth exploring to see what fits your specific workflow. Check out the full list at https://github.com/modelcontextprotocol/servers

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Top MCP tools for software architects

Introduction

What is MCP?

1. AWS MCP

2. GitHub MCP

3. Incident.io MCP

4. Atlassian MCP

5. IcePanel MCP

6. Honeycomb MCP

7. Terraform MCP

8. MCP Toolbox for Databases

9. Grafana MCP

10. Notion MCP

Conclusion

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