"Graph Visualization Techniques and Tools" 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.

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.

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.

Enables coding agents to query a deterministic AST knowledge graph of a codebase over MCP—finding symbols, callers/callees, dependencies, impact, and relevant tests via compact path:line JSON—so they can navigate code without grepping, embeddings, or an LLM.

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 Claude Code and other MCP clients to query a local, explainable knowledge graph of a codebase: answering questions, explaining symbols, tracing paths between them, and computing blast-radius impact, with every edge backed by file:line evidence. Read-only and fully offline — no network calls, telemetry, or runtime dependencies.

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 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.

Serves approved lessons learned from past corrections to coding agents, delivering task context and file-scoped lessons before work starts and checking finished work against them before the agent declares done. It also exposes module facts, dependency impact, and past misses tied to specific files.

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.

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

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.