"An overview of temporal knowledge graphs" matching MCP servers:

Enables developers and product owners to scan a codebase and extract a readable business overview—features, roles, access control, data model, and endpoints—along with findings on missing or inconsistent rules, and generate shareable HTML or Markdown reports and diagrams.

An experimental MCP server that compresses git commit co-change coupling graphs using the Baligács temporal clique linear spanner algorithm, reducing edge and token counts by ~55-65% while preserving temporal reachability, and exposes tools like index_repository, get_architecture, search_code, and temporal_compress for querying code knowledge graphs.

A local knowledge graph MCP server that provides AI agents with permanent, structured memory about codebases, enabling semantic search, blast radius analysis, and convention enforcement.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

Provides a project memory bank and RAG context provider for enhanced code understanding and management through vector embeddings, integrated with RooCode and Cline.

A high-precision local knowledge base server enabling AI agents to navigate, search, and reason about complex codebases using hybrid semantic, lexical, and graph retrieval.

Provides GDB and clangd-based tools for debugging C++ programs, enabling step execution, variable inspection, diagnostics, and combined runtime-static analysis.

An MCP server that extends AI coding assistants with deterministic, algorithmic capabilities such as code analysis, fault localization, and formal verification, enabling an autonomous engineering team within the IDE.

Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.

A comprehensive Model Context Protocol server that integrates Vibe Coding methodology with Knowledge Graph technology for AI-assisted software development using Kuzu embedded database.

A local-first, LLM-agnostic MCP server that lets you ask hard questions about your documents, media, and code, and get traceable answers entirely offline.

Enables fast code analysis and navigation through hybrid semantic search, graph-based relationship tracking, and structure exploration across multiple programming languages with optimized indexing for large codebases.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Enables token-efficient semantic search and analysis over any directory of files through hybrid search, directory overview, structural analysis, and dependency graphs.

Enables automatic project understanding and context injection for OpenCode and Antigravity, detecting project type and refreshing knowledge incrementally without per-project setup.

Validates Mermaid diagrams with comprehensive grammar parsing supporting 28+ diagram types. Processes markdown files, ZIP archives, and direct input with detailed error reporting and enterprise-grade performance capabilities.

Enables Claude to access and search UVM 1.2 source code, class hierarchies, and method implementations, providing expert-level knowledge of the Universal Verification Methodology.

MCP server that provides AI assistants with structured access to codebases via LogicStamp Context, enabling component analysis, dependency graphs, drift detection, and token-optimized context delivery.

Compiler introspection for the Hirð language (typed actors and effect rows on the BEAM): type inference, definition lookup, effect-row explanation, IR fragments, actor protocol and supervision graphs, and token-budget-aware symbol summaries.

Serves token-efficient code context for AI coding assistants by exposing Python signatures, call graphs, and budget-packed task context as MCP tools, enabling precise codebase queries without reading entire files.