"Graph Visualization Tools for Nodes and Edges" matching MCP servers:

Enables querying a TypeScript codebase's graph for call flows, type relationships, and symbol locations without reading file bodies.

A knowledge graph MCP server that indexes your codebase into a local SQLite graph and provides tools for project mapping, file summaries, and impact analysis, drastically reducing token usage in AI coding assistants.

A local knowledge graph of your codebase for Claude Code on macOS, exposing structural data via MCP tools for querying calls, imports, test coverage, and impact analysis.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

Indexes a mono-repo into a knowledge graph and provides MCP tools to query code structure—packages, components, routes, HTTP calls—without file reads or grep round-trips.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

A high-performance code knowledge graph server implementing MCP, indexing codebases into a structured AST knowledge graph with semantic search, call graph traversal, and HTTP route tracing.

A local MCP server that indexes codebases into Neo4j and provides tools for code analysis, dependency visualization, and impact assessment.

Enables exploring a C++17 coroutine and device-domain call graph through MCP tools, providing symbol search, caller/callee queries, and neighborhood exploration of coroutine-aware codebases.

A function-level call-graph indexer for GDScript/Godot projects, exposed as an MCP server so AI coding assistants can answer questions like 'what calls this function?' or 'what would break if I change this signal?' without re-reading the whole codebase.

Enables Claude Code to query a TypeScript dependency graph and retrieve the minimal set of files needed for code review, implementation, or debugging.

A TypeScript code graph tool with MCP integration that enables AI agents to perform semantic search and graph traversal (call graphs, paths between symbols) across a codebase.

Turns a codebase into a queryable graph with semantic search, call graphs, and control/data flow analysis, served to AI coding agents via the Model Context Protocol.

Builds a semantic knowledge graph of C++ code and exposes 9 MCP tools for AI assistants to search classes, functions, inheritance, callers, callees, overrides, and more.

Enables querying cross-repo code dependencies, HTTP routes, database tables, and queues via an MCP server using Cypher queries.

MCP server for indexing source code from repositories into a Neo4j graph database and enabling Graph RAG-based search and traversal of functions via natural language queries.

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

An MCP server that exposes a Svelte/SvelteKit project's component dependency graph over stdio, enabling AI coding assistants to query component imports, unused components, and route dependencies without an editor.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

Knowledge graph for token-efficient code reviews. Builds a structural map of your codebase with Tree-sitter, tracks changes incrementally, and gives AI agents precise context via MCP tools. Features fixed multi-word search, qualified call resolution, dual-mode embedding (ONNX local + LiteLLM cloud), and output pagination.