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A Model Context Protocol server that analyzes application codebases with real-time file watching, providing AI assistants like Claude with deep insights into project structure, code patterns, and architecture.

Provides comprehensive codebase analysis including project structure evaluation, cross-language duplicate detection, microservices validation, and configuration optimization with AI-powered pattern learning that generates actionable improvement reports.

An extended MCP server for managing code snippets, performing code analysis, and handling deployments via Render and GitHub. It enables users to perform code reviews, track issues, and manage service operations through natural language interactions.

An MCP server that offloads bulk coding tasks to local LLMs, allowing Claude Code to delegate repetitive work like boilerplate generation and code polishing while preserving its context for complex reasoning.

A cognitive scaffolding platform that helps AI agents break down complex tasks into manageable steps using hierarchical planning and metacognitive guidance. Provides persistent memory, progress tracking, and intelligent pattern recognition to learn from successful project structures.

Enables AI assistants to perform comprehensive code quality checks including pylint, pytest, and mypy analysis on Python projects, with smart prompts for explaining issues and suggesting fixes.

Provides intelligent code context management and semantic search capabilities for software development, enabling natural language queries to find relevant code snippets, functions, and classes across Python, JavaScript, TypeScript, and SQL codebases.

Indexes codebases into a SQLite database to provide metadata, exports, dependency graphs, and change tracking for JS/TS projects. It enables users to search for symbols, map internal dependencies, and monitor file changes through MCP tools and a web dashboard.

Persistent codebase knowledge layer for AI agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) and serves via MCP. 28 languages, 14 tools, ~85% token reduction.

Enables safe, concurrent file system operations with sandboxed directory access control, automatic encoding detection, optimistic locking, and precise code editing capabilities including search-replace and batch operations.

Provides sandboxed code execution for AI agents with support for Python, JavaScript, and shell commands. Includes comprehensive safety features like destructive pattern blocking, timeout protection, and restricted file access for secure production use.

A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.

A self-hosted MCP server that provides a single execute_code tool, enabling agents to write TypeScript to call multiple REST APIs via fetch() with transparent credential injection, reducing token usage by keeping intermediate results in the sandbox.

Indexes local Python code into a Neo4j graph database to provide AI assistants with deep code understanding and relationship analysis. Enables querying code structure, dependencies, and impact analysis through natural language interactions.

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.

CodeGraph — Open-source code intelligence MCP server. Builds a semantic graph of your codebase (functions, classes, imports, call chains) and exposes it through 31 tools. Callers, callees, impact analysis, complexity metrics, unused code detection, AI context assembly, persistent memory, cross-project search. 15 languages via tree-sitter. Single Rust binary, local-first.

The only Multi-LLM Compliance Engine (GPT-4o + Claude + DeepSeek). Auto-fix GDPR/LGPD risks and more 15 frameworks. code.guard.eu

Provides centralized security instructions for AI-assisted code generation by matching context-aware rules to the user's programming language and file patterns. It ensures generated code adheres to security best practices without requiring manual maintenance of instruction files across individual repositories.

Provides production insights and code analysis by leveraging Nexus instrumentation data to identify CPU-intensive methods and visualize execution flow trees. It enables developers to make data-driven decisions through real-time performance metrics and automated service discovery from Java class names.

Deterministic context selection for AI coding agents. Scores and selects the minimal file set for each task, then records outcomes into a local ledger that compounds into reusable patterns.