MCPIgnore Filesystem Server

Overview

The MCPIgnore Filesystem server extends the baseline Filesystem MCP Server by adding a data‑security layer that leverages files—an adaptation of the well‑known syntax. In environments where an AI assistant must interact with a local or networked file system, the risk of accidental disclosure or modification of sensitive files is high. This server allows developers to declare precisely which files and directories an MCP client may read, write, or otherwise manipulate. By placing a file in any directory that is exposed to the client, developers can protect environment variables, private keys, proprietary models, or any other confidential assets without changing the underlying server code.

The core functionality remains identical to the standard Filesystem MCP Server: reading and writing files, creating, listing, deleting, and moving directories, searching for files, and retrieving metadata. The only difference is that each tool’s operation first checks the patterns for the target path. If a pattern matches, the tool is blocked; otherwise it proceeds as normal. For example, a call that attempts to access will be denied if an in the root directory contains . Operations that only return file names—such as and —are permitted, ensuring that clients can still discover the structure of allowed directories without exposing protected content.

Key features include:

• Pattern‑based exclusion: Supports comments, negations, and globbing just like , making it familiar to developers.
• Granular control: Each directory can host its own , allowing different teams or projects to enforce distinct security policies.
• Transparent integration: The server requires no changes to MCP clients; they simply receive “block” responses for disallowed paths.
• Full filesystem API: All standard file operations are available, ensuring that developers do not lose functionality while gaining safety.

Typical use cases involve AI assistants that generate code, edit configuration files, or process data sets. In a corporate setting, the assistant might be allowed to read and modify only the directory of a repository while keeping , , and other sensitive files hidden. In research labs, large language models can safely explore datasets on a shared server without risking accidental data leaks. The server’s ability to restrict access at the file level also enables compliance with privacy regulations such as GDPR or HIPAA by preventing the model from accessing personally identifiable information.

Integrating this server into an MCP workflow is straightforward: developers add the entry to their client’s configuration (e.g., Claude, Cline, or Cursor) and specify the root directories via . The client then communicates with the server over the standard MCP protocol; any attempt to access a path that matches an ignore pattern results in a blocked response, while all other operations proceed normally. This seamless integration ensures that developers can adopt the security model without modifying existing client code or changing their interaction patterns.

In summary, the MCPIgnore Filesystem server empowers developers to harness the full power of AI‑assisted file manipulation while maintaining strict control over sensitive data. By combining a familiar ignore syntax with the robust capabilities of the base Filesystem MCP Server, it delivers a practical, low‑overhead solution for secure AI workflows.