"Project dependency resolution and analysis" matching MCP servers:

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

A cross-project knowledge graph for MCP that maps features across mobile, backend, and admin codebases, giving AI agents full-stack context.

Enables AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

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.

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 users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

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

MCP server that analyzes codebases to provide dependency graphs, impact analysis, and file insights across 15+ programming languages, enabling AI assistants to understand project structure and navigate code efficiently.

Enables deterministic Markdown spec dependency tracking, version review, change impact analysis, and audit through a fixed-project MCP stdio interface.

Offline MCP server for SAP ABAP static analysis, ABAP Cloud readiness scoring, and RAP scaffolding. It enables AI agents to lint ABAP source, compare reworks, check released APIs, and generate RAP business objects without needing an SAP system.

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

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.

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 assistants to interact with Autodesk Civil 3D, allowing them to retrieve project data, create/modify/delete drawing elements, and execute code to automate Civil 3D operations.

🐍 🏠 - 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

An MCP server that turns a rough project idea into a structured spec, running entirely locally inside any MCP-compatible editor.

Gives AI agents instant access to software architecture, dependencies, and impact analysis through pre-computed sgraph models, replacing dozens of grep/read cycles with a single tool call.

A research proxy that lets Claude, Cursor, and other MCP clients utilize Gemini's generous free tier for deep codebase analysis without burning your main model's quota!

MCP server that provides a live, terminal-native layered dependency map for AI coding assistants, with tools to track and visualize development progress in real time.

MCP server for comprehensive code analysis, navigation, and quality assessment across 25+ programming languages.