Frank Goortani CV MCP Server – Overview

The Frank Goortani CV MCP Server is a dedicated Model Context Protocol (MCP) endpoint that exposes a professional résumé in a machine‑readable format. By turning a CV into a structured data source, the server enables AI assistants and other MCP‑compatible tools to query, filter, and present a candidate’s background without manual parsing. This eliminates the need for custom scraping or proprietary APIs, allowing developers to integrate rich résumé information into conversational agents, talent‑management platforms, or interview preparation tools with minimal effort.

At its core, the server implements a set of tools that return specific slices of the CV—profile details, skill sets, interests, and work history—as JSON objects. These tools are invoked through the MCP protocol over either standard I/O or HTTP, giving developers flexibility in how they host and consume the service. A markdown resource is also exposed (), providing the full résumé as plain text for scenarios that require a complete document view or further transformation into PDFs, slides, or web pages.

Key capabilities include:

• Targeted queries: Retrieve only the profile, skills, or interests, enabling concise responses in chat applications.
• Search across all sections: A search tool scans the entire CV, returning relevant excerpts for keyword‑driven queries.
• Company filtering: Extract experience specific to a given employer, useful for recruiters or hiring managers comparing candidates.
• Media links: Direct URLs to the PDF resume and profile picture allow AI agents to share downloadable assets or embed images in outputs.

These features make the server ideal for real‑world use cases such as:

• Recruitment chatbots that can answer candidate questions or pull up résumé details on demand.
• Interview assistants that surface relevant work experience during candidate discussions.
• Personal branding tools where an individual can expose their résumé to multiple platforms via a single, version‑controlled source.

Because the server is built on FastMCP and configured for deployment to Cloudflare Workers, it can run globally with low latency while requiring no server‑side maintenance. The dual transport support (stdio and HTTP) further simplifies integration into local development environments or distributed cloud services. Overall, the Frank Goortani CV MCP Server transforms a static résumé into an interactive, AI‑ready asset that streamlines knowledge sharing and enhances the intelligence of conversational agents.