"A tool for locating all usages and references of a symbol in a codebase" matching MCP servers:

Provides a code graph querying and visualization MCP server, enabling users to search, trace, and describe code components, overlay diffs, and explore architecture through natural language.

Analyzes software projects to extract architecture, build dependency graphs, and predict the impact of code changes.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

Provides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.

Enforces a rigorous spec-driven engineering workflow for AI coding assistants, requiring requirements, design, and task planning before any code is written.

Provides UI architecture analysis for converting Figma designs or web pages into reusable UI code blocks with effort estimation in CSV format.

Enables structural knowledge-graph code search, AST snippet retrieval, architecture analysis, call-path tracing, and textual code search across indexed repositories, plus an auto-started graph visualization UI.

An MCP server that helps transfer C4 diagrams from DrawIO and PlantUML into a DocHub architecture repository.

Enables AI IDEs to chat with, search, explain, refactor, and audit a local codebase without uploading source code, returning answers backed by file-and-line source citations, confidence scores, and severity labels, plus audit and board-ready strategic reports. Runs locally and standalone as an MCP server compatible with Claude, Cursor, Windsurf, and DeepSeek Harness.

Translates natural language commands into structured geometric operations for a simulated CAD engine using LLMs. It enables users to create shapes, modify dimensions, and perform 3D operations like extrusion through the Model Context Protocol.

Enables coding agents to draw and edit Excalidraw diagrams on localhost by rendering Mermaid source into a live-updating canvas synced through a context file, with support for reusable icon libraries. No API key is required.

Enables interactive MATLAB development by allowing users to execute scripts and specific code sections, manage workspace variables, and capture generated plots. It integrates with the MATLAB Python Engine to provide persistent execution context for MCP-compatible clients.

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.

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.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Provides AI agents with a live architecture model of a codebase, enabling queries for root cause analysis, blast radius, and dependency traversal through MCP tools.

Enables visual collaboration with an AI agent on a codebase through a shared drawing canvas, where hand-drawn diagrams are inferred as editable structure over MCP.

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 generation of PlantUML ArchiMate enterprise architecture diagrams with full ArchiMate 3.2 specification support across all 7 layers, including automatic validation, multi-format export (PNG/SVG/XML), and instant diagram viewing through a built-in HTTP server.

Enables source-backed discovery of application purpose, architecture dependencies, field usages, and SQL transformations and value lineage. It provides cited evidence, separates confirmed facts from interpretations, and exposes coverage gaps, with optional offline local-model reasoning.

An MCP server that gives Claude Code a curated atlas of your codebase, fusing LSP-grade structure with architectural intent from ADRs, git history, and test associations for single-call context bundles.

A local MCP server that gives AI coding agents symbol definitions, dependency graphs, and a live architecture vocabulary for TypeScript/JavaScript repos, with no network or embeddings.

Serves the architectural graph of a hexagonal-architecture codebase to coding agents via stdio, providing tools to analyze dependencies, check violations, and inspect structural changes.

Enables agents to publish Mermaid diagrams by submitting them through a serve_design tool, which stores them in a companion web service and returns a shareable URL where the rendered diagram can be viewed.

Enables agents to generate a JSON sitemap of how a path through a TypeScript/JavaScript codebase runs, capturing the summary, areas, every step with its file, lines, description, calls and data, and the links between services. The resulting document can be opened in a viewer that draws the same map as a single page, and rebuilt in CI to keep system documentation current.

Turns a codebase into business-language flow diagrams that an AI can read and edit, enabling natural language querying and modification of system architecture.

Turns a source tree into a resolved map of files, symbols, references, and dependencies. Ask how components connect, what a change can break, then get bounded source evidence and copyable next steps.

Enables any MCP client to query an eaten codebase as a compiled graph, answering reachability, blast-radius, dependents, and call-chain questions through walk tools instead of loading source files.

CodeGraph MCP is a powerful standalone tool that parses your entire C/C++ codebase into a semantic knowledge graph and seamlessly exposes it to AI coding assistants via the Model Context Protocol (MCP). By providing AI (like Claude Desktop, Cursor, or Google Antigravity) with a structural map of your project—including caller/callee relationships, file dependencies, and dynamic function definition

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.