"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.

MCP server that lets Claude Code, Codex or opencode hand coding work to cheaper models run by opencode.

Enables MCP clients to invoke Cursor SDK's agent runtime, run coding agents, list models, and continue conversations.

Provides free, unlimited access to Claude AI models via Puter.js for AI coding agents and IDEs through the MCP protocol.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

MCP server that analyzes videos locally with frame-by-frame understanding and optional browser sidecars, producing structured context bundles for coding agents to fix bugs, add features, or build new functionality.

Enables MCP clients to run Cursor CLI agent prompts with any model the installed CLI offers, continue existing sessions, and list available models and sessions. Adds guarded execution with concurrency limits, timeouts, cancellation, and operator-gated auto-approval so multiple models can be run in parallel.

Enables coding agents to install and manage Paydirt's iOS SDK for user feedback, trial cancellation, and subscription cancellation, with Slack integration and app building.

Enables AI assistants to consult other AI models via OpenRouter for help with coding tasks.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Add OpenAI (Codex) models to Claude code

Gives coding agents structural understanding of a codebase: it indexes Java and Python repositories to resolve symbols, call sites with confidence tiers, module dependencies, diff impact, and hotspots. It also ships an eval harness that measures whether this context actually helps an agent.

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

A Model Context Protocol (MCP) server providing 62 AI-optimized tools for .NET/C# semantic code analysis, navigation, refactoring, and code generation using Microsoft Roslyn. Built for AI coding agents - provides compiler-accurate code understanding that AI cannot infer from reading source files alone.

Provides IDE-like semantic code retrieval and editing tools for LLMs, enabling precise code understanding and manipulation in large codebases via the Model Context Protocol.

Enables building Flutter and Dart apps through a multi-agent system that orchestrates specialized agents for UI design, backend development, testing, and deployment.

An MCP server leveraging the Rust-based ty type checker to provide AI models with high-performance, semantic Python code analysis and structural navigation. It enables precise symbol searching, cross-file renaming, and diagnostic reporting to improve code understanding and editing accuracy.

A specialized toolchain that guides AI agents through a structured 'Atomic Development' workflow for building Python FastAPI and Supabase backends. It manages project scaffolding and enforces dependency-ordered generation of database models, API routes, and tests.

Production-grade, autonomous Model Context Protocol (MCP) server that elevates AI models from stateless code generators into persistent, self-verifying software engineers.

Bridges MCP clients to OpenCode's headless API, enabling AI to delegate autonomous coding tasks like building features, debugging, and refactoring across multiple projects.

Bridges Claude Desktop with Claude Code CLI to delegate complex coding tasks like creating React apps, building APIs, and debugging scripts while maintaining interaction through the Desktop interface.

Enables agents to explore a static RTTI, call, and cross-reference graph of a game executable, querying functions, classes, vtables, strings, globals and memory through search, disassembly, signature and SQL tools. It also supports a provenance-tracked naming lifecycle where labels are proposed with evidence, reviewed, and ported across game patches.

Allows Claude Code to offload AI coding tasks to Aider, reducing costs and enabling more control over which models handle specific coding tasks.

An MCP server that enables Claude and other LLM clients to access Hanzo AI platform capabilities and provide powerful code management tools for analyzing, modifying, and understanding projects.

Enables coding agents to review GitHub pull requests with a panel of AI models through local or remote MCP endpoints.

Enables MCP clients such as Claude Code to use the latest Codex CLI as a tool, starting new Codex threads and continuing them with replies, including approvals handling and progress notifications. It keeps the classic codex and codex-reply tools working while letting users swap in newer Codex binaries to unlock the newest models.

Enables coding agents to check human-stated assumptions against authoritative sources like AWS AppConfig, Sentry, and the repository, detecting when beliefs have drifted from reality. It provides tools to inspect belief history, list verifiable properties, review open drifts, and record verified understanding.

Launches the official Codex CLI as a persistent MCP server so DeepSeek Harness can invoke Codex models without storing or transmitting ChatGPT credentials to third parties.

An AI-agent-first framework for building MCP servers that deliver interactive React widgets directly within AI chat interfaces like ChatGPT and Claude. It includes automated visual testing and a zero-config local development environment designed for autonomous agent workflows.

Enables Claude to hand off implementation tasks to OpenAI Codex models running through the Codex CLI, with tools for listing models, delegating work synchronously or in the background, collecting results, and continuing past sessions, while Claude stays the orchestrator that reviews diffs and runs tests.