"Using local LLMs for code writing, reviewing, and rule generation" matching MCP servers:

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

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

This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.

A private-by-default, local-first DAG editor that lets you build causal diagrams visually, validate cycles, and save/export as JSON, SVG, or PNG through MCP tools.

Enables a coding agent to inspect Go HTTP endpoints and their MySQL table interactions, view SQL/source evidence, and collaboratively create or update diagram tabs on a shared browser canvas via MCP.

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

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.

An MCP server that analyzes and traces business code flows across your codebase, enabling developers to quickly understand the complete execution path of features from frontend to backend to database.

Enables local LLMs to perform enterprise-grade code auditing, AST security screening, architectural planning, and real-time Discord observability with zero cloud API costs.

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.

Analyzes runtime data objects to suggest optimal UI component instructions for a low-code renderer using structural heuristics. It automatically maps data patterns to specific UI elements like tables, carousels, and cards to streamline interface generation.

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.

Enables AI agents and reviewers to query a deterministic code dependency graph for callers, routes, pages, tables, and config with file:line evidence instead of grepping. One server can serve many indexed graphs, supporting cross-repository impact analysis.

MCP server that provides code intelligence by indexing configured repositories, extracting deterministic facts, and enabling graph queries, drift checks, and maps via Kuzu-backed MCP tools.

Infers the architecture rules a TypeScript repo already follows from its import graph, with the evidence for each rule. Read-only tools to scan, recommend and explain rules, so an agent can check them before writing code.

A design orchestrator that brings Superdesign capabilities to Claude Code as native tools, enabling AI-powered UI design, wireframing, component creation, and logo/icon generation without requiring Anthropic API keys.

Turns relational schemas into editable entity-relationship diagrams in draw.io, supporting live databases, SQL DDL, YAML/JSON specs, and existing .drawio files, with tools for managing tables, columns, and relations.

Enables AI agents to analyze and scaffold React/Next.js applications enforcing Feature-Sliced Design (FSD) conventions, providing tools for project analysis, rule retrieval, and entity generation.

Analyzes Java Spring Boot Maven repositories by scanning source code, building a dependency graph, and exposing tools for architecture overview, change impact, dependency cycles, rule validation, and refactoring targets so LLMs can answer architecture questions without rereading the codebase.

Generates GCP architecture diagrams, sequence diagrams, flow charts, and class diagrams using Python diagrams DSL via MCP.