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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An MCP server that executes Python code in isolated rootless containers with optional MCP server proxying. Implementation of Anthropic's and Cloudflare's ideas for reducing MCP tool definitions context bloat.
| Date | Stars |
|---|---|
| 2026-07-31 | 338 |
| 2026-08-05 | 338 |
| 2026-08-13 | 337 |
| 2026-08-18 | 337 |
| 2026-08-25 | 338 |
| 2026-09-02 | 339 |
| 2026-09-14 | 339 |
| 2026-09-15 | 340 |
| 2026-09-19 | 341 |
| 2026-09-20 | 341 |
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# MCP Code Execution Server: Zero-Context Discovery for 100+ MCP Tools [](https://mseep.ai/app/elusznik-mcp-server-code-execution-mode) **Stop paying 30,000 tokens per query.** This bridge implements Anthropic's discovery pattern with rootless security—reducing MCP context from 30K to 200 tokens while proxying any stdio server. [](https://www.anthropic.com/engineering/code-execution-with-mcp) [](https://blog.cloudflare.com/code-mode/) [](https://www.docker.com/blog/dynamic-mcps-stop-hardcoding-your-agents-world/) [](https://machinelearning.apple.com/research/codeact) [](https://modelcontextprotocol.io/) [](https://mseep.ai/app/4a84c349-1795-41fc-a299-83d4a29feee8) ## Overview This bridge implements the **"Code Execution with MCP"** pattern, a convergence of ideas from industry leaders: - **Apple's [CodeAct](https://machinelearning.apple.com/research/codeact)**: "Your LLM Agent Acts Better when Generating Code." - **Anthropic's [Code execution with MCP](https://www.anthropic.com/engineering/code-execution-with-mcp)**: "Building more efficient agents." - **Cloudflare's [Code Mode](https://blog.cloudflare.com/code-mode/)**: "LLMs are better at writing code to call MCP, than at calling MCP directly." - **Docker's [Dynamic MCPs](https://www.docker.com/blog/dynamic-mcps-stop-hardcoding-your-agents-world/)**: "Stop Hardcoding Your Agents’ World." - **[Terminal Bench](https://www.tbench.ai)'s [Terminus](https://www.tbench.ai/terminus)**: "A realistic terminal environment for evaluating LLM agents." Instead of exposing hundreds of individual tools to the LLM (which consumes massive context and confuses the model), this bridge exposes **one** tool: `run_python`. The LLM writes Python code to discover, call, and compose other tools. ### Why This vs. JS "Code Mode"? While there are JavaScript-based alternatives (like [`universal-tool-calling-protocol/code-mode`](https://github.com/universal-tool-calling-protocol/code-mode)), this project is built for **Data Science** and **Security**: | Feature | This Project (Python) | JS Code Mode (Node.js) | | :--- | :--- | :--- | | **Native Language** | **Python** (The language of AI/ML) | TypeScript/JavaScript | | **Data Science** | **Native** (`pandas`, `numpy`, `scikit-learn`) | Impossible / Hacky | | **Isolation** | **Hard** (Podman/Docker Containers) | Soft (Node.js VM) | | **Security** | **Enterprise** (Rootless, No Net, Read-Only) | Process-level | | **Philosophy** | **Infrastructure** (Standalone Bridge) | Library (Embeddable) | **Choose this if:** You want your agent to analyze data, generate charts, use scientific libraries, or if you require strict container-based isolation for running untrusted code. ## What This Solves (That Others Don't) ### The Pain: MCP Token Bankruptcy Connect Claude to 11 MCP servers with ~100 tools = **30,000 tokens** of tool schemas loaded into every prompt. That's **$0.09 per query** before you ask a single question. Scale to 50 servers and your context window *breaks*. ### Why Existing "Solutions" Fail - **Docker MCP Gateway**: Manages containers beautifully, but still streams **all tool schemas** into Claude's context. No token optimization. - **Cloudflare Code Mode**: V8 isolates are fast, but you **can't proxy your existing MCP servers** (Serena, Wolfram, custom tools). Platform lock-in. - **Academic Papers**: Describe Anthropic's discovery pattern, but provide **no hardened imple
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:634b9d999c7252d9, topic:mcp, topic:model-context-protocol, name:mcp server
matched fp:634b9d999c7252d9, topic:agents, topic:agentic-ai