MCP File Finder

Overview

The MCP File Finder server provides a lightweight, HTTP‑based service that lets AI assistants locate files on a filesystem using simple text queries. Rather than requiring developers to write custom file‑search logic, the server exposes a single endpoint that accepts a directory and a search string, returning structured JSON with file metadata. This eliminates the need for AI clients to manage filesystem traversal or to embed complex globbing logic, enabling a cleaner separation between the assistant’s natural‑language reasoning and the underlying data retrieval.

For developers building AI workflows, this server is especially valuable when an assistant must reference local resources—such as source code snippets, configuration files, or documentation—to answer user questions. By delegating the search to a dedicated MCP server, developers can keep their AI model focused on language understanding while offloading the deterministic file‑system operations to a reliable service. The server’s simple query parameters ( and ) make it easy to integrate into existing MCP clients: a single tool invocation can return all matching files, which the assistant can then inspect or display.

Key capabilities of MCP File Finder include:

• Text‑based file matching: Search for files whose names contain a given substring, case‑sensitive by default.
• Directory scoping: Limit the search to a specified root directory, preventing accidental traversal of protected areas.
• Rich metadata: Each result includes the file’s absolute path, size in bytes, and creation timestamp, giving downstream logic context for filtering or prioritization.
• Stateless REST interface: The service runs on a standard Flask server, making it straightforward to deploy behind a reverse proxy or within containerized environments.

Typical use cases span several real‑world scenarios. In code review assistants, the server can quickly locate all test files or configuration scripts that match a pattern described in natural language. Documentation bots may retrieve relevant markdown or XML files when answering “Where can I find the deployment guide?” In data science pipelines, an assistant could fetch all CSV files in a dataset folder that contain a particular keyword. Because the server returns JSON, the assistant can immediately parse and format results for presentation or further processing.

Compared to ad‑hoc scripting, MCP File Finder offers a consistent API that aligns with the Model Context Protocol’s expectations for tools. Its lightweight design means it can run locally on a developer’s machine or be exposed through an internal network, ensuring low latency and high reliability. By encapsulating file‑search logic in a dedicated MCP server, developers gain a reusable component that scales with their AI projects and keeps the assistant’s codebase clean and focused on language tasks.