MCP TextFiles Server

Overview

The MCP_TextFiles server provides a lightweight, file‑system interface for AI assistants. It exposes three core capabilities—listing, reading, and writing plain text files within a specified local directory. By converting simple file operations into MCP resources, the server lets Claude or other AI clients treat a local folder as an extensible knowledge base that can be queried, updated, and expanded on the fly.

This solution addresses a common pain point for developers building AI‑augmented workflows: persisting state or user data without requiring a full database stack. Instead of juggling SQL schemas, REST endpoints, and authentication layers, developers can point the server at a directory and immediately gain read/write access through the MCP tool set. The result is an effortless bridge between local file storage and conversational AI, ideal for prototyping or lightweight production use.

Key features include:

• Resource enumeration – The server lists all files in the target directory, allowing AI assistants to discover available documents and their metadata (size, last modified time).
• Content retrieval – A dedicated tool fetches the entire contents of a selected file, returning plain text that can be incorporated into prompts or further processed by the assistant.
• Atomic updates – The write tool accepts a filename and new text payload, overwriting the file if it exists or creating a new one. This ensures that user edits are immediately reflected on disk.
• Security controls – By limiting the exposed route to a single directory, the server minimizes accidental exposure of sensitive files. Developers can further harden access with environment variables or host‑level permissions.

Typical use cases span from personal knowledge bases (e.g., a daily journal or meeting notes stored as text files) to dynamic configuration management (e.g., updating feature flags or environment variables via AI). In a CI/CD pipeline, an assistant could read build logs, suggest fixes, and write patch files directly. For educational tools, students can interact with coding exercises stored as plain text, receiving instant feedback and updated solutions.

Integration into an AI workflow is straightforward: the MCP server registers its tools under a recognizable name (e.g., ). A client then invokes these tools within prompts, passing file names or content as arguments. The assistant can chain operations—list files, read the most recent one, analyze its content, and write a summary back to disk—all within a single conversational turn. This tight coupling removes the friction of external file handling and keeps the developer’s focus on business logic rather than plumbing.

In summary, MCP_TextFiles turns a local directory into an AI‑friendly knowledge store. Its simplicity, security by design, and seamless integration with MCP clients make it an attractive choice for developers who need reliable text storage without the overhead of a traditional database or API layer.