MCP Web UI

Overview

MCP Web UI is a web‑based host that implements the Model Context Protocol (MCP) to streamline interactions between AI assistants and large language models. By acting as a single entry point, it aggregates context from multiple LLM providers, handles prompt engineering, and coordinates streaming responses—all while maintaining a coherent conversation history. For developers building AI‑powered applications, this server removes the need to manage separate client libraries for each model provider and instead offers a unified interface that adheres to MCP standards.

The server’s core value lies in its context‑aware architecture. It stores conversation history in a lightweight BoltDB database, enabling seamless context retrieval and incremental updates. When an AI client requests a new turn, MCP Web UI packages the relevant chat history into a single prompt and forwards it to the chosen LLM provider. The response is streamed back via Server‑Sent Events (SSE), giving users a real‑time, chat‑like experience without the latency of full round‑trips. This design is particularly useful for building conversational agents that require low‑latency feedback loops, such as customer support bots or interactive coding assistants.

Key features include:

• Multi‑Provider Integration – Support for Anthropic, OpenAI, Ollama, and OpenRouter in one place. Switching models is as simple as changing a configuration entry.
• Dynamic Configuration – Runtime‑editable settings for temperature, top‑p/k, token limits, and provider‑specific parameters. Developers can fine‑tune behavior per session or globally without redeploying.
• Persistent History – BoltDB persistence keeps chat logs across restarts, enabling continuity for long‑running conversations or audit trails.
• Advanced Context Aggregation – The server automatically prunes and compresses older messages, ensuring that prompt size stays within provider limits while preserving essential context.
• SSE Streaming – Real‑time token delivery keeps UI responsive and reduces perceived latency, especially important for large models that generate lengthy responses.

Typical use cases span from developer tooling (embedding the UI in IDE extensions to provide on‑the‑fly code explanations) to customer service (quickly switching between GPT and Claude for different support tiers). In research settings, the server can act as a sandbox where multiple models are evaluated side‑by‑side under identical prompts, facilitating comparative studies. Because MCP Web UI adheres to the MCP protocol, it can be easily chained with other MCP hosts or tools, enabling complex workflows such as data retrieval, code execution, or policy enforcement to be composed in a single conversational flow.

In summary, MCP Web UI offers developers a robust, protocol‑compliant hub that unifies multiple LLM providers, manages context efficiently, and delivers a smooth, streaming chat experience—all while keeping configuration simple and extensible.