"Houdini" matching MCP servers:

Enables AI assistants to control SideFX Houdini 3D software, providing tools for scene management, node operations, rendering, and more through the Model Context Protocol.

An MCP server for controlling SideFX Houdini via hrpyc, enabling AI assistants to interact with Houdini sessions.

Multi-protocol IPC adapter layer for integrating DCC software (Maya, Houdini, Blender, Unreal, Unity) with MCP, enabling tool registration and remote control across multiple transport protocols.

Lets Claude Code or any MCP client hand bounded work such as code review, boilerplate generation, commit messages, and format conversion to a local GPU model, an OpenAI-compatible cloud API, or a LiteLLM/OpenRouter router, so those files never enter the orchestrator's context window. This cuts token cost by roughly 93% on read-heavy tasks and provides a second model for reviewing code, while the main AI platform keeps the planning and judgement calls.

Enables Claude Code and other MCP clients to delegate bounded tasks such as code review, boilerplate generation, commit messages, and format conversion to a local or cloud OpenAI-compatible LLM. It keeps the main model as architect while cutting token costs by sending files and prompts straight to the second model instead of into the orchestrator's context window.

It enables Claude or any MCP client to delegate bounded tasks such as code review, boilerplate generation, commit messages, format conversion, and embeddings to local or cloud OpenAI-compatible LLMs, reducing token usage and providing a second opinion.

Enables Claude Code and other MCP clients to hand bounded work — code review, test stubs, commit messages, docs, format conversion, mock data, embeddings — to a local GPU model or a cheap cloud/OpenAI-compatible endpoint, so file contents are sent straight to that model instead of entering the main AI platform's context window. This cuts token spend dramatically while giving users a second model for reviewing code, with the orchestrating assistant still checking every result.

Enables Claude Code and other MCP clients to offload bounded work — code review, boilerplate, commit messages, format conversion, embeddings — to a local or cloud OpenAI-compatible LLM, cutting token spend by keeping large files out of the orchestrator's context. It also provides a second model for reviewing code and drafting tasks while the main assistant retains planning and QA.

Enables Claude and other MCP clients to hand off bounded work — code review, test stubs, boilerplate, commit messages, docs, format conversion, embeddings — to local or cloud OpenAI-compatible models while the main assistant keeps planning and QA. Keeps large source files out of the orchestrator's context window, cutting token costs and providing a cheap second opinion on code.

Enables Claude Code and other MCP clients to offload bounded tasks to a local or cloud LLM (LM Studio, Ollama, vLLM, LiteLLM, OpenRouter, DeepSeek, Groq, Cerebras, OpenAI) so files are read and drafted outside the orchestrator's context window, cutting token costs. It also provides a cheap second model for code review, test stubs, commit messages, format conversion, and embeddings.

Enables Claude or other MCP clients to offload bounded tasks such as code review, boilerplate, commit messages, and format conversion to local or cloud OpenAI-compatible models, saving tokens and providing a second opinion. It supports endpoints including LM Studio, Ollama, vLLM, OpenRouter, OpenAI, DeepSeek, Groq, and Cerebras.

Enables MCP clients such as Claude Code to delegate bounded tasks like code review, boilerplate generation, commit messages, format conversion, and embeddings to local or cloud OpenAI-compatible LLMs, reducing token costs while keeping the main model in control.

Enables Claude or other MCP clients to delegate bounded coding work—such as code review, test stubs, commit messages, format conversion, and embeddings—to local or cloud OpenAI-compatible models while reducing token usage. It keeps the main AI platform as the orchestrator and has returned work reviewed by that platform.

Enables MCP clients such as Claude Code to hand bounded tasks — code review, test stubs, boilerplate, commit messages, format conversion and embeddings — to a local GPU model or a cheaper cloud or router endpoint over any OpenAI-compatible API, so bulky file content never enters the orchestrator's context window. The primary model stays the architect and QA, while token spend drops and a second model becomes available for reviewing code on demand.

An MCP server that lets an MCP client offload bounded tasks such as code review, boilerplate, test stubs, commit messages and format conversion to a local LLM or a cheaper cloud model, keeping the source files out of the main model's context and cutting token costs. It also provides a second model for reviewing code, generating embeddings and drafting content, with the orchestrating client checking all returned work.

Enables Claude Code and other MCP clients to offload bounded tasks such as code review, test stubs, commit messages, and format conversion to local or cheaper cloud LLMs, cutting token usage while the primary model remains the orchestrator.

Enables an MCP client to offload bounded tasks such as code review, boilerplate generation, commit messages, and format conversion to a local or cloud LLM, keeping those files out of the main model's context window to cut token costs. It also provides a cheap second model for reviewing code and drafting content while the primary model handles planning and tool use.

Enables MCP clients such as Claude Code to delegate bounded coding and review tasks to local or cloud OpenAI-compatible LLMs, reducing token costs and providing a second model opinion. The main orchestrator retains planning and tool use while offloading drafting, code review, test stubs, documentation, and embeddings.

An MCP server that lets Claude Code or any MCP client hand bounded work — code review, boilerplate, commit messages, format conversion, embeddings — to a local GPU model, an OpenAI-compatible endpoint, a router, or a cheap cloud API, so those files never enter the orchestrator's context window. This cuts token spend and quota use while providing a second model to sanity-check results.

This MCP server enables Claude or any MCP client to offload bounded coding and documentation tasks to local or cloud OpenAI-compatible LLMs, reducing token usage and providing an extra model for review. It supports code review, boilerplate generation, commit messages, format conversion, mock data, and embeddings.