Anbani MCP Server

Overview

The Anbani MCP Server is a specialized Model Context Protocol (MCP) implementation that exposes the full functionality of the Anbani Georgian Language Toolkit to AI assistants. It solves the problem of language‑specific tooling fragmentation by providing a single, standardized interface through which Claude and other AI agents can access linguistic resources, perform morphological analysis, and generate Georgian language content. For developers building conversational or generative systems that need deep linguistic insight into Georgian, this server removes the need to embed complex language models or libraries directly in the client application.

At its core, the server offers a set of MCP resources that expose key capabilities of the toolkit: tokenization, part‑of‑speech tagging, lemmatization, and syntactic parsing. Each capability is wrapped in a lightweight HTTP endpoint that adheres to the MCP schema, allowing AI assistants to request analysis or generation with minimal latency. The server also supports prompt templates that guide the assistant’s output, ensuring that responses adhere to Georgian linguistic conventions and stylistic guidelines. By leveraging MCP’s sampling features, developers can fine‑tune the randomness of generated text, balancing creativity with grammatical correctness.

Key features include:

• Unified API surface for all linguistic operations, reducing boilerplate in client code.
• Scalable request handling, enabling batch processing of multiple sentences or documents without compromising performance.
• Extensible resource registry that allows future expansion to additional Georgian sub‑tasks such as named entity recognition or sentiment analysis.
• Secure, token‑based authentication that integrates with existing MCP security frameworks.

Typical use cases span a broad spectrum of applications:

• Language learning platforms can provide instant, context‑aware feedback on student essays.
• Chatbots for Georgian customer support can generate natural, grammatically correct replies.
• Content generation tools may produce culturally appropriate marketing copy or literary text in Georgian.
• Data annotation pipelines can automatically tag large corpora before manual review.

Integration into AI workflows is straightforward: a developer configures the assistant’s MCP client to point at the Anbani server, selects the desired resource (e.g., ), and passes text data. The assistant receives a structured JSON response that can be consumed directly or fed into downstream processing steps, such as summarization or translation. Because the server implements standard MCP sampling semantics, developers can easily experiment with different temperature settings to control output variability.

What sets the Anbani MCP Server apart is its focus on native Georgian linguistic expertise within a proven MCP framework. Unlike generic language models that may produce syntactically correct but culturally off‑target text, this server guarantees adherence to Georgian grammatical rules and stylistic norms. Its modular design also means that as the Anbani toolkit evolves—adding new linguistic features or improving performance—the MCP interface can be updated without disrupting client integrations. For developers who need reliable, high‑quality Georgian language processing in AI applications, the Anbani MCP Server offers a robust, standards‑compliant solution that bridges domain expertise with modern conversational AI.