Managing AI Agent Context Without the Bloat

As teams adopt AI coding assistants like Cursor, Windsurf, or Claude Code, they hit a common scaling problem: context management.

At first, you start with a custom .cursorrules file or system prompt containing your repository guidelines. This works well for a single folder or a small team. But as your codebase grows and your team ships changes daily, two problems emerge.

First, your static configuration files drift. You rename a database utility or update a styling convention, but you forget to update the rules file. The AI agent relies on the stale instructions and writes invalid code.

Second, your context window bloats. If you try to combat drift by feeding the AI entire folders of documentation, you consume thousands of tokens on every single prompt. This slows down generation speed and inflates API costs.

Serving dynamic context via Model Context Protocol (MCP) solves these issues by shifting from push-based context (sending everything upfront) to query-based context (letting the agent ask for what it needs).

The Limits of Static Rules#

A static configuration file like .cursorrules is a snapshot. In a fast-moving team, snapshots decay quickly.

For example, if your team switches from one schema validation library to another, every developer knows the new convention. But if the static prompt is not updated, the AI coding agent will continue generating code using the old validation pattern.

Furthermore, static instructions are global. An agent working on a database migration does not need to read your UI styling rules. Feeding the agent unnecessary guidelines wastes context tokens. For large codebases, this overhead makes agent runs slow and expensive.

Shift to Query-Based Context with MCP#

Model Context Protocol (MCP) is an open standard that allows developers to expose secure data sources to AI models. Instead of stuffing every guideline into a static file, you connect the agent to an MCP server.

When the agent starts a task, it queries the MCP server for specific, relevant information. For instance, the agent can call a tool to retrieve active database schemas, naming conventions, or documentation gaps.

This approach keeps context usage minimal. The agent only reads what is directly relevant to the current file or change.

How Moxie Docs Automates the Context Layer#

At Moxie Docs, we built a hosted MCP server that eliminates the need to maintain static rule files.

When you connect a GitHub repository, Moxie indexes your codebase tree, package manifests, and code conventions. Every time a pull request is merged, Moxie updates the index.

Instead of writing custom prompts, you generate a repository-scoped MCP token. In Cursor or Claude Desktop, you add the Moxie Docs endpoint. The AI agent can now call tools like moxie.get_conventions or moxie.search_docs to fetch the exact guidelines it needs.

If your team updates a naming pattern or database helper, Moxie detects the change on the next merge. The next time your AI agent queries the MCP server, it receives the updated convention.

Keeping the Loop Clean#

Using dynamic context keeps your codebase documentation alive and ensures your AI tools write code that actually matches your current production standards. You save token costs, prevent hallucinations, and avoid the chore of manually editing static rule files.