Overview of the CTX MCP Server

The CTX MCP server is a purpose‑built context management tool designed to bridge the gap between evolving codebases and large language models (LLMs). In modern software projects, files are added, modified, or removed constantly. Every time a developer wants the LLM to reason about the current state of the code, they must regenerate context that reflects these changes. CTX automates this regeneration process by allowing developers to declare exactly which parts of the codebase should be exposed to the model, thereby eliminating guesswork and improving both security and efficiency.

At its core, CTX parses a declarative YAML configuration that lists documents to be generated. Each document specifies a description, an output path, and one or more sources—typically file collections filtered by directory, filename patterns, or other attributes. The server then walks the file system, gathers matching files, and compiles them into a single structured document. These documents are stored under the directory, ready for ingestion by any LLM that supports the MCP protocol. By controlling the scope of context at this granularity, developers can prevent accidental leakage of sensitive code and ensure that the assistant only sees what is truly relevant to the task at hand.

Key capabilities include:

• Fine‑grained source filtering: Select files by directory, glob patterns, or custom predicates.
• Declarative configuration: Define multiple context documents in a single YAML file, making the process repeatable and version‑controlled.
• Automatic regeneration: Trigger context rebuilds whenever the underlying files change, keeping LLM inputs up to date without manual intervention.
• Structured output: Produce clean Markdown or other text formats that can be directly fed into prompts, improving prompt quality and reducing noise.

Typical use cases span a wide range of development scenarios. A backend engineer can generate an authentication module context before asking the LLM to refactor security logic, while a front‑end team might build a UI component context for design review. Continuous integration pipelines can also invoke CTX to produce fresh contexts before running automated code reviews or testing assistants. In all cases, the server ensures that the AI’s knowledge is tightly scoped to the current code state, making interactions more reliable and reducing the risk of hallucinated or outdated information.

By integrating seamlessly with existing MCP‑enabled assistants, CTX empowers developers to maintain full control over what the LLM sees. Its declarative approach and automated regeneration make it a standout solution for teams that demand predictability, security, and efficiency in AI‑assisted software development.