"A server for creating cloud architecture diagrams" matching MCP servers:

Enables AI coding agents to list, read, create, update, delete, and render PlantUML and Mermaid diagrams, and check them against the actual codebase to detect architecture drift.

Enables scanning Java/Maven projects to generate TOGAF-standard architecture deliverables (Excel and PPTX) via natural language commands.

Compare evidence-linked documentation claims with facts observed in the current source tree.

Enables AI assistants to retrieve a project's architecture overview and file structure, auto-detecting monolith or multiproject layouts and exposing module listings to reduce token consumption.

Analyzes GitHub and local repositories to automatically generate visual architectural diagrams such as dependency graphs, class diagrams, and data flow diagrams.

Analyzes Java Spring Boot Maven repositories by scanning source code, building a dependency graph, and exposing tools for architecture overview, change impact, dependency cycles, rule validation, and refactoring targets so LLMs can answer architecture questions without rereading the codebase.

MCP server for the Codex Security Cloud service, enabling agents to list, inspect, close, reopen findings, request PRs, and apply generated patches.

Enables AI assistants to read Bitbucket Cloud pull requests and diffs through natural conversation.

An MCP server for evaluating C# project architecture quality using the Modularity Maturity Index (MMI) framework. It provides tools for detecting layering violations, circular dependencies, and encapsulation issues with interactive D3.js visualizations.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables AI agents to analyze and search IFS Cloud codebases using hybrid semantic-lexical search, PageRank importance ranking, and dependency graph analysis. It supports comprehensive exploration of PL/SQL, entities, and projection files to facilitate intelligent architectural understanding.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

Audits dbt Core projects for migration blockers and generates actionable guidance for migrating to dbt Cloud, including auto-fixing deprecated syntax.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Enables MCP-compatible AI clients to inspect Godot project and editor state, observe opted-in running scenes, manage processes, and apply permission-scoped guarded edits. It starts read-only and exposes additional capabilities only through explicit command-line flags.

Uses Claude as an independent second-model reviewer inside Codex, performing reviews on disposable Git snapshots with structured findings.

Enables MCP clients to list workspaces and inspect concise, workspace-scoped code review findings and hosted run summaries. It can also prepare a hosted review for an open pull request and, after explicit user confirmation, start or rerun it, without exposing raw diffs, reports, or credentials.

Enables coding agents to locate the right files and conventions in a Next.js project in a single call, drastically reducing token usage compared to searching.

Enables an MCP client to sign in and inspect workspace-scoped review findings and hosted runs, and to prepare, then start or rerun after explicit confirmation, an existing hosted code review for a pull request. It exposes only concise summaries, never raw diffs, full reports, artifacts, credentials, or prompt text.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Codebase Q&A agent with git-history awareness. Explore a repo with list_files, read_file, and search, plus git log, blame, and diff and a repo map to answer "why did this change?" questions. Runs locally as an MCP server for Claude Code and other MCP clients.

A remote, OAuth-protected server that lets MCP clients list workspaces, inspect concise review findings and hosted run summaries, and prepare or, after explicit confirmation, start and rerun existing hosted reviews without exposing raw diffs or full reports.

Enables authenticated MCP clients to browse their Juror workspaces, inspect workspace-scoped review findings, and summarize open P0/P1 issues. It can also prepare and, after explicit user confirmation, start or rerun an existing hosted Juror Cloud review, without exposing raw diffs, reports, or credentials.

Inspects workspace-scoped review findings and hosted runs, and can prepare and, after explicit user confirmation, start or rerun an existing hosted review. It avoids exposing raw diffs, full reports, artifacts, screenshots, credentials, or arbitrary local repository access.

Enables an OAuth-signed-in user to list their workspaces and inspect concise, workspace-scoped review findings and hosted run summaries. It can also prepare a pull request review and start or rerun an existing hosted review once the user explicitly confirms.

Enables MCP-compatible clients to list workspaces, inspect workspace-scoped review findings and hosted run summaries, and prepare, then after explicit user confirmation start or rerun, hosted reviews without exposing raw diffs, artifacts, or credentials.

Enables MCP clients to inspect workspace-scoped code review findings and hosted runs, then prepare and, after explicit confirmation, start or rerun existing hosted reviews. It provides concise review summaries without exposing raw diffs, full reports, artifacts, screenshots, credentials, or prompt text.

Enables MCP clients to inspect workspace-scoped findings and hosted runs, and to prepare or, after explicit confirmation, start or rerun existing hosted reviews without exposing raw diffs, full reports, credentials, or prompt text.