Consult7 MCP Server

Overview

Consult7 is a Model Context Protocol (MCP) server designed to bridge the gap between AI assistants with limited context windows and large, complex data sets such as full codebases or document repositories. By aggregating files from user‑specified paths and concatenating them into a single, massive context, Consult7 forwards the combined content to high‑capacity models (OpenRouter, OpenAI, or Google) that can ingest far more tokens than typical assistants. The response is then returned directly to the agent, allowing developers to perform deep analyses that would otherwise exceed the assistant’s native limits.

This capability is especially valuable for developers working with sophisticated AI tools like Claude, where the default context window (e.g., 200 k tokens) cannot accommodate an entire application’s source code. With Consult7, a single query can trigger a model to read and understand thousands of files, summarize architecture, locate specific functions, or evaluate test coverage—all without manual pagination or data slicing. The server’s design keeps the workflow seamless: the assistant sends a request, Consult7 collects and streams the relevant files, the chosen provider processes them, and the assistant receives a concise answer or a file‑based report.

Key features include:

• Wildcard path support for flexible file selection across directories and languages.
• Provider‑agnostic integration, letting users choose between OpenRouter, OpenAI, or Google models.
• Thinking mode ( suffix) that encourages step‑by‑step analysis, useful for security reviews or complex logic flows.
• Output file handling, enabling large reports to be written directly to disk instead of flooding the agent’s context.
• Test mode for validating API connectivity before deployment.

Typical use cases span the software development lifecycle: summarizing entire projects, pinpointing method implementations across mixed‑language codebases, assessing test coverage gaps, performing security threat modeling, and generating comprehensive code review reports. In research or documentation settings, the server can ingest academic papers or policy documents to produce high‑level insights or cross‑referenced summaries.

By offloading heavy context processing to external large models, Consult7 extends the practical reach of AI assistants. Developers can maintain a single conversational interface while still accessing deep, context‑rich analyses—making it an indispensable tool for teams that need to interrogate large codebases or document collections efficiently and reliably.