Overview

The Mcp Sage server is a Model Context Protocol (MCP) service that bridges the gap between Claude‑centric workflows and large‑context AI models such as GPT‑5, Gemini 2.5 Pro, and Claude Opus 4.1. By automatically selecting the most suitable model based on token count and available API keys, it allows developers to maintain their existing Claude Code experience while leveraging the expanded context windows of newer models for more complex codebases. This capability is particularly valuable when a single prompt must reference thousands of lines or entire directories, something that Claude’s default context size cannot handle efficiently.

At its core, Mcp Sage exposes two practical tools: and . Both accept a user prompt or instruction along with file paths, recursively compress those files into an XML‑like structure, and then send the combined payload to the chosen model. The server’s token‑aware selection logic ensures that small contexts (≤ 400K tokens) use GPT‑5 for its superior reasoning, medium contexts (≤ 1M tokens) route to Gemini 2.5 Pro, and a fallback to GPT‑4.1 guarantees service continuity when keys are missing or token limits are exceeded. The response is returned directly to the AI assistant, ready for further processing.

The tool goes a step further by wrapping the model’s output in blocks. This format is immediately actionable, allowing developers to apply suggested code changes with minimal friction. When the flag is set, both tools initiate a structured multi‑model debate: each model generates, critiques, and synthesizes responses in parallel. This iterative dialogue often yields higher‑quality insights than a single pass, mimicking a human review panel.

Use cases abound:

• Large‑scale code reviews where the assistant must analyze an entire repository before offering feedback.
• Design discussions that require a model to consider hundreds of files for architectural decisions.
• Automated refactoring where the assistant proposes concrete search‑and‑replace patches across a codebase.
• Cross‑model collaboration, enabling teams to harness the strengths of multiple providers without manual switching.

Integration is seamless for MCP‑aware assistants. A single tool invocation in a prompt—specifying file paths and optional debate mode—activates the entire workflow. The assistant receives a polished, context‑rich answer without exposing any of the underlying token calculations or API key logic. This abstraction frees developers to focus on their core tasks while still benefiting from the most powerful large‑context models available.