"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.

An MCP server that gives your agent a persistent project brain: vision, architecture decisions, conventions, roadmaps, and automatic session handoff.

Exposes a Neo4j knowledge-graph index over personal markdown notes to LLM agents via MCP, enabling semantic search, tag navigation, and relationship discovery.

Graph-MIND records every conversation you have with Claude Code, Codex and Claude Desktop, word for word, in a store on your own PC. When a question depends on the past, the model you are using calls Graph-MIND over MCP and gets back a few thousand tokens of the conversations that answer it.

Exposes a dependency graph of strategic sourcing artifacts and analyses as an MCP server, enabling navigation of phases, analyses, deliverables, and graph traversal for impact analysis and build order.

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 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.

Provides AI coding agents with a local knowledge-graph memory, enabling cross-project retrieval via PageRank diffusion and tools to retrieve, write, extract, update, and view recent knowledge over MCP.

Enables LLMs to perform OSINT link-analysis by exposing transforms (DNS, WHOIS, Shodan, etc.) as MCP tools for autonomous investigation and graph enrichment.

Provides a decision memory layer for codebases, enabling AI agents to search, retrieve, and record business decisions with metadata filters and full-text search.

Maps Salesforce metadata into a Neo4j graph database, enabling AI agents to reason about schema and generate accurate SOQL queries.

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 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.

MCP server that turns coding workflows into explicit execution graphs (DAG) with deterministic command execution, human-in-the-loop gates, and tamper-proof run history for Claude Code.

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.

Graph-relational memory MCP server for persistent LLM memory, featuring wikilinks, BM25 auto-linking, spreading activation, and synaptic decay across five memory layers.

Provides Claude Desktop with persistent, structured memory and semantic search via a local SQLite knowledge graph, plus a web UI for visualization and management.

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.

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.