MCP App

Overview

The MCP App is a ready‑made Model Context Protocol server that equips AI assistants with dynamic retrieval and web‑search capabilities. By exposing RAG (Retrieval‑Augmented Generation) tools, the server lets a large language model pull in fresh information from a vector store and even add new documents on the fly, effectively expanding the knowledge base it can reference during conversations. This solves a common pain point for developers: keeping an AI assistant up‑to‑date without redeploying the entire model.

For developers working with Claude or other MCP‑compatible assistants, the server provides a clean interface for two essential tasks. First, it offers vector‑based retrieval using PGVector and PostgreSQL, so queries can be answered with the most relevant documents stored in a structured database. Second, it supplies web‑search tools that fetch real‑time data from the internet, allowing assistants to respond with current facts or niche information not present in the local store. The combination of these tools means that a single AI session can seamlessly switch between long‑term knowledge and live data, improving accuracy and relevance.

Key features of the MCP App include:

• RAG tooling: Retrieve, rank, and embed documents from a PostgreSQL database with PGVector, enabling the model to reference up‑to‑date content.
• Dynamic document addition: Users can insert new documents during a session, which the server immediately indexes and makes searchable.
• Web search integration: Built‑in web‑search tools powered by the Tavily API allow real‑time queries to external sources.
• MCP‑ready server: Exposes resources, tools, and prompts in the standard MCP format so any compliant client can consume them without custom adapters.
• SQLAlchemy ORM: Simplifies database interactions, making schema management and data manipulation straightforward for developers.

Typical use cases span from customer support bots that need to pull the latest policy documents, to research assistants that must browse scholarly articles on demand. In a knowledge‑heavy environment like legal or medical domains, the ability to add new case studies or clinical guidelines on the fly can dramatically reduce answer latency and improve compliance. Moreover, developers building conversational agents for e‑commerce can use the web‑search tool to fetch current product prices or availability, ensuring that recommendations are always accurate.

Integrating the MCP App into an AI workflow is straightforward: a client such as Claude Desktop can be pointed to the server’s endpoint, and the assistant will automatically discover the RAG and web‑search tools. The server handles all vector indexing, query execution, and tool invocation behind the scenes, freeing developers to focus on higher‑level conversational logic. Its modular design also means that additional tools or custom prompts can be added later, making the MCP App a scalable foundation for any project that requires intelligent, up‑to‑date data retrieval within an AI assistant.