面试鸭 MCP Server

Overview

The 面试鸭 MCP Server brings the popular Chinese interview‑prep platform 面试鸭 into the Model Context Protocol ecosystem, becoming the first domestic site to expose its question‑search API via MCP. This integration solves a common pain point for developers building AI assistants: accessing high‑quality, domain‑specific knowledge bases without writing custom connectors. By presenting interview questions as structured resources that can be queried, filtered, and returned in a consistent format, the server lets assistants like Claude, Cursor, or 千帆AppBuilder retrieve relevant problems on demand.

At its core, the server exposes a single tool called . When an assistant receives a natural‑language prompt such as “Show me a data‑structures interview question about trees,” the tool receives the query string and returns a Markdown link to the matching 面试鸭 page. This straightforward interface hides the complexities of HTTP requests, pagination, and authentication, allowing developers to focus on higher‑level conversational logic. The tool’s output is intentionally simple—a clickable link—so that downstream applications can embed it directly into chat messages, dashboards, or learning portals.

Key capabilities include:

• MCP‑compatible API: The server follows the official MCP specification, making it plug‑and‑play with any compliant client. No custom SDKs are required beyond the standard MCP Java SDK.
• Rich resource discovery: By leveraging 面试鸭’s search engine, the tool can surface thousands of interview questions across topics, difficulty levels, and company categories.
• Developer‑friendly integration: The server can be launched as a standalone Java process or embedded within Spring Boot applications. Configuration is declarative, using JSON files that specify command lines and environment variables.
• Seamless workflow integration: Assistants can invoke as part of a multi‑step reasoning chain, combining it with other tools (e.g., code generators or data fetchers) to provide comprehensive interview preparation support.

Real‑world scenarios for this MCP server include:

• Interview coaching platforms: Embed the tool into a chatbot that guides candidates through tailored practice sessions, automatically pulling relevant questions from 面试鸭.
• Learning management systems: Use the server to populate lesson plans with up‑to‑date interview questions, ensuring students work on current industry topics.
• Recruitment analytics: Analysts can query the server to retrieve question sets used by specific companies, facilitating benchmarking and trend analysis.

The standout advantage of this MCP server lies in its domain specificity combined with a minimalist interface. By converting complex search logic into a single, well‑documented tool, developers can rapidly prototype AI assistants that deliver precise interview content without wrestling with third‑party APIs or data scraping. This makes the 面试鸭 MCP Server an essential component for any AI‑driven hiring or education product that needs authoritative, searchable interview resources.