Riza MCP Server – An Isolated Code Interpreter for AI Assistants

The Riza MCP Server bridges the gap between large language models (LLMs) and a secure, sandboxed execution environment. It exposes Riza’s powerful code‑execution API as a set of MCP tools, allowing AI assistants to write, store, and run code without exposing the host system or user data to potential security risks. For developers building AI workflows, this means they can delegate code generation and execution responsibilities to a trusted third‑party service while retaining full control over the tools’ lifecycle.

What Problem Does Riza Solve?

When an LLM produces code, executing it locally can be dangerous: the code might contain malicious payloads, inadvertently corrupt data, or consume excessive resources. Traditional approaches require developers to build custom sandboxes or rely on cloud functions that add latency and complexity. Riza offers a ready‑made, isolated interpreter that runs code in a contained environment managed by Riza’s infrastructure. By integrating this through MCP, developers can offload execution entirely while keeping the workflow transparent to the assistant.

Core Features and Value

• Tool Creation & Persistence – lets an LLM generate a reusable script and store it via Riza’s Tools API. The resulting tool can be invoked later, ensuring consistent behavior across sessions.
• Tool Retrieval & Editing – With and , the assistant can review, modify, or update existing tools. This is especially useful for iterative development or when the model needs to refine a routine.
• Secure Execution – and run code in Riza’s sandboxed interpreter. The former runs a stored tool, while the latter allows ad‑hoc code snippets without persisting them. Both operations return structured results, making it easy for the assistant to incorporate outputs back into the conversation.
• Tool Discovery – provides a catalog of all available tools, enabling the assistant to suggest relevant scripts or check for duplicates before creating new ones.

These capabilities turn Riza into a full‑featured code execution hub that is both secure and easy to manage, eliminating the need for custom sandboxing logic in client applications.

Use Cases & Real‑World Scenarios

• Data Analysis Pipelines – An assistant can generate a Python script that pulls data from an API, processes it with Pandas, and returns visualizations. The script is stored as a tool and can be reused across multiple conversations.
• Automated Testing – Developers can write unit tests via the assistant, store them as tools, and run them on demand. Results are returned in a structured format for quick feedback.
• Rapid Prototyping – When experimenting with new algorithms, the assistant can produce code snippets that are immediately executed in Riza’s sandbox, providing instant output without risking local environments.
• Continuous Integration – CI workflows can trigger calls to run build or deployment scripts, ensuring that all code runs in a consistent, isolated environment.

Integration with AI Workflows

MCP clients such as Claude Desktop can be configured to point to the Riza server, automatically exposing its toolset. Once integrated, the assistant can reference these tools by name in natural language instructions (“Run the tool”), and the MCP layer handles the communication with Riza’s API. Because each tool is a discrete endpoint, developers can add or remove capabilities without redeploying the assistant itself. The result is a modular architecture where code execution becomes a first‑class citizen in the AI workflow.

Unique Advantages

• Zero Trust Execution – By delegating code execution to Riza’s sandbox, developers avoid the overhead of building and maintaining their own secure runtimes.
• Persistent Tool Library – Unlike transient code execution services, Riza stores tools in a searchable repository, enabling reuse and versioning.
• Seamless MCP Integration – The server’s toolset is already wrapped as MCP endpoints, meaning no additional glue code is required to expose them to LLMs.
• Scalable & Managed Infrastructure – Riza handles scaling, resource allocation, and security updates, allowing developers to focus on product logic rather than infrastructure.

In summary, the Riza MCP Server equips AI assistants with a safe, reusable, and easily accessible code execution environment. It streamlines the development of intelligent workflows that require dynamic programming capabilities while safeguarding against security risks and operational complexity.