steipete/oracle

Ask the oracle when you're stuck. Invoke GPT-5 Pro with a custom context and files.

What it solves

Oracle is designed for developers and coding agents who need a second-model review of their project. It solves the problem of manually copying and pasting files into an AI model to get a review or audit, by automating the bundling of a prompt and selected project files into a context window.

How it works

Oracle operates as a CLI and an MCP server. It allows users to select files, directories, or globs (with exclusions) to create a a context bundle. This bundle is then sent to an AI model via an API (supporting OpenAI, Azure OpenAI, Anthropic, Gemini, xAI, and OpenRouter) or through a signed-in browser session (using Chrome automation for ChatGPT and Gemini).

Who it’s for

It is built for developers and coding agents that require grounded, project-specific AI reviews and audits.

Highlights

  • Flexible Context Control: Supports files, directories, and globs with exclusions to precisely define the context.
  • Multiple Engine Modes: Offers API mode for automation, Browser mode for using existing signed-in sessions, and Render mode for local inspection of the prompt bundle.
  • Session Management: Stores runs in local sessions, allowing users to reattach to long responses, replay answers, and perform follow-up queries.
  • Multi-Model Support: Can run a single prompt across multiple models simultaneously to compare outputs and track costs.
  • MCP Integration: Includes an oracle-mcp stdio server for integration with MCP clients like Claude Code, Codex, and Cursor.

Related

  • Project
    oracle/mcp

    A collection of reference MCP servers that enable AI models to interact with Oracle Cloud Infrastructure and Oracle products through a standardized protocol.

  • Project
    apify/mcpc

    A universal CLI client for the Model Context Protocol (MCP) that enables AI agents and developers to interact with MCP servers via standard shell commands.

  • Project
    microsoft/mcp

    A collection of Model Context Protocol (MCP) servers and core tooling that standardizes how AI agents and LLMs connect to Microsoft services and external data sources.

  • Project
    1mcp-app/agent

    A unified runtime for MCP servers that aggregates multiple servers into one process and provides a progressive discovery workflow to prevent agent context sprawl.

  • Project
    googleapis/gcloud-mcp

    A collection of Model Context Protocol (MCP) servers that enable AI assistants to interact with Google Cloud via the gcloud CLI and various cloud APIs using natural language.