"Oh Dear" matching MCP servers:

Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.

Enables coding agents to run deterministic, zero-dependency AST static analysis over source code, building call graphs and dependency metrics to find orphaned functions, unreachable methods, breaking API surface diffs, and token-bloat clusters, then return a structured refactoring plan. It plugs into MCP clients like Claude Desktop, Cursor, and Windsurf via a single Python entrypoint.

Tells you how you and your Claude agent actually work together. Reads CLAUDE.md, hooks, skills, memory + scheduled tasks and writes you a letter. Local-only, no API keys, no data leaves your machine.

AI-powered Python refactoring assistant that provides AST-based code analysis, SOLID violation detection, dead code elimination, type safety analysis, performance optimization, and automated refactoring with rollback capabilities.

Provides comprehensive Python code refactoring capabilities including AST-based analysis, automated refactoring, and performance optimization through the Model Context Protocol.

Pingpong is an MCP server for oh-my-pi that gives your AI agent a local code reviewer using a local LLM (llama.cpp) for automated code reviews after task completion.

Enables auto-configuration and validation of React components against a glassmorphic design token system using an AST correction engine. It supports managing UI presets, enforcing design uniformity in Monaco IDE, and exporting tokens as CSS, Tailwind, or JavaScript variables.

Enables AI-assisted X++ development for Dynamics 365 Finance and Operations by pre-indexing the entire codebase and providing 54 specialized tools for metadata lookup, code generation, and best practice validation.

Put your node_modules on a diet — find unused dependencies, analyze package health, and reduce size.

Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.

Semantic code search for Claude Code, enabling natural language codebase indexing and search using AI embeddings.

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

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

Detects and automatically removes AI-generated code slop patterns like redundant comments, verbose logging, and unnecessary error handling across multiple programming languages.

Enables AI coding agents to reverse engineer an operating system from its ISO and rebuild it component by component, exposing 39 tools over stdio for ISO fingerprinting, sandboxed QEMU VM lifecycle, PE analysis, API-surface dumps, syscall tracing, behavior diffs, component scaffolding, compat shims, app-compat testing, and knowledge-base search. It lets an agent survey a reference OS, generate clean-room specs and code, boot the rebuild, and test real applications against it.

A FastMCP-based server that provides secure sandboxed filesystem operations, API integration tools, and curated reasoning prompts for analysis and productivity tasks.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

An experimental MCP server offering calculator, GitHub repository fetching, a movie database resource, and JavaScript code review capabilities. Designed for learning and compatible with Cursor and Claude Desktop.

MCP server for local codebase memory: semantic indexing, hybrid search, and clean code analysis (static + LLM) with retrievable project notes, designed to run fully local on modest hardware.

Enables deterministic AST static analysis and call-graph auditing to identify orphaned functions, unreachable methods, and token-bloat clusters, producing refactoring plans that reduce context waste.