RAE MCP Server

The Royal Spanish Academy (RAE) Model Context Protocol server bridges the gap between advanced language models and authoritative linguistic data. By exposing RAE’s dictionary, etymology, usage examples, and normative rules through the MCP interface, it enables AI assistants to retrieve precise, up‑to‑date Spanish language information on demand. This eliminates the need for models to rely solely on pre‑trained knowledge, which can become stale or incomplete, especially when dealing with evolving usage or specialized terminology.

At its core, the server offers two practical tools: search and get_word_info. The search tool allows a model to query the RAE API with any string, returning concise results that highlight matching entries and contextual snippets. The get_word_info tool dives deeper, providing a structured response that includes definitions, grammatical categories, synonyms, antonyms, and example sentences. Both tools support an optional language parameter (defaulting to Spanish), making the server adaptable for multilingual contexts where RAE resources might be queried alongside other language databases.

Developers can run the server in two modes. The stdio transport is ideal for tight integration with LLM runtimes that read and write JSON over standard input/output, enabling seamless tool invocation without network overhead. The SSE (Server‑Sent Events) mode exposes the same capabilities over HTTP, allowing web applications or distributed services to subscribe to tool responses in real time. This flexibility ensures that the RAE MCP server can fit into a wide range of AI pipelines, from local chatbot deployments to cloud‑based conversational agents.

Real‑world use cases include educational platforms that need authoritative Spanish definitions, content moderation systems verifying correct usage, or translation services that must reference normative spellings and idiomatic expressions. Because the server queries RAE’s official API directly, it guarantees consistency with the Academy’s latest updates—an essential feature for compliance‑driven or academic applications. Moreover, by leveraging the MCP standard, developers can swap out or augment this server with other linguistic resources (e.g., dictionaries for different languages) without altering the model’s core logic, fostering modular and maintainable AI ecosystems.