"Understanding the concept of dependency" matching MCP servers:

Enables scanning repositories for hardcoded service URLs and clients, and building service-to-service API dependency maps.

Plan your projects with The High Council! Several AI models from different labs argue about your idea, then debate on Software Engineering choices. The goal is to get everyone to work together, in harmony and with manners. Effectiveness is unproven, but it is a fun way to spend your tokens!

Enables coding agents to analyze Python source with AST-based static analysis, detect circular dependencies across monorepo packages via Tarjan SCC, and produce a topological build order with Kahn's algorithm along with refactoring plans and graph metrics. Runs with zero external dependencies over MCP from Claude Desktop, Cursor, or any compliant client.

Enables auditing and drafting of AI agent files (CLAUDE.md, AGENTS.md, Cursor rules) and turning project descriptions into full build specs, with local and API-based tools.

A Model Context Protocol (MCP) server that provides tools for interacting with OpenROAD and ORFS (OpenROAD Flow Scripts).

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

Validates and renders declarative JSON diagram specs to themeable SVG. The format has no coordinates, so a model writes structure and the renderer does the layout; tools: validate, render, kinds.

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 rendering, validation, formatting, and inspection of D2 diagrams via the Model Context Protocol, using WASM for rendering without requiring the d2 binary.

A Model Context Protocol server that gives an LLM a structured visual layer to describe what exists and get back a clean, precise SVG diagram instead of ASCII art.

An MCP server that builds a local webpage to view AI plans, reports, and diagrams, so the AI only needs to send a URL pointer instead of long text. It provides three tools (view_plan, view_report, view_diagram) and tracks project changes for diff viewing.

Enables generating, reading, auditing, and previewing BPMN 2.0 diagrams for Bizagi Modeler, with an automatic layout engine that produces files Bizagi can open, plus optional control of the Bizagi Modeler desktop app on Windows.

Enables auto-configuration and validation of React components against a glassmorphic design token system using an AST correction engine. It supports managing UI presets, enforcing design uniformity in Monaco IDE, and exporting tokens as CSS, Tailwind, or JavaScript variables.

A read-only MCP server that gives AI agents access to Sparx Enterprise Architect models, enabling search, navigation, and reading of elements, packages, scenarios, and connectors from .qea exports without a running EA instance.

Model Context Protocol (MCP) server for @imqueue — lets AI coding agents (Claude Code, Cursor and others) search the docs, scaffold typed services & clients and use @imqueue/cli live.

Generates UML and other diagrams (30+ types) via the Model Context Protocol, supporting multiple output formats and deployment options.

Enables converting ASCII and Unicode text diagrams into crisp, animated SVG with 1:1 verification, including repair of misaligned diagrams.

AI-native architecture diagramming for MCP clients (Claude Code, Cursor, Windsurf). The calling agent authors the graph (nodes, groups, edges); Flowgraf validates it, lays it out deterministically with ELK, renders an SVG + Mermaid, and returns a link to a live, editable canvas you can refine by chat, drag, or one-click AI design review. Agent-authored ops — no API key and no LLM cost to the calle

Enables AI coding agents to retrieve mode and stage guidance so they can lead users through a 9-stage design workflow, including questions, required artifacts, exit criteria, dependencies, pitfalls, rollback impact, and environment requirements. It helps non-designers turn ideas into buildable HTML/WPF design specs while keeping the human in control of confirmation and revisions.

🐍 🏠 - Code graph for AI agents over MCP: resolves Python and TypeScript calls to fully qualified names and reports the share it could not resolve instead of guessing. Multi-hop impact analysis, authz reachability audit, architectural drift against declared patterns. Local, SQLite. uvx codegraph-brain