Saqr-MCP

Saqr-MCP is a versatile Model Context Protocol (MCP) server that bridges local and cloud AI models with a rich set of tooling designed for real‑world productivity. It resolves the common pain point of having to juggle separate APIs and libraries for web search, memory storage, document generation, and reasoning—all within a single, coherent MCP interface. Developers can therefore expose sophisticated AI capabilities to assistants like Claude or GPT without writing custom adapters for each external service.

At its core, Saqr-MCP offers a dual‑model backend: local models via Ollama and cloud models through Groq. This flexibility lets teams choose between the speed and privacy of on‑prem inference or the cutting‑edge performance of cloud providers, simply by swapping a configuration flag. The server exposes an array of tools that augment the model’s reasoning pipeline: real‑time web search powered by Tavily, Word document creation from markdown, and a memory layer built on mem0. Each tool is implemented as an MCP endpoint, so the assistant can invoke them declaratively within its prompt or via tool calls.

Key features include:

• Interactive chat client that demonstrates the MCP flow end‑to‑end, with async handling for low latency.
• Advanced web search that returns fresh data, enabling assistants to answer time‑sensitive queries.
• Word document generation that turns markdown or plain text into polished .docx files, useful for report automation.
• Comprehensive memory management through mem0, allowing the assistant to store, retrieve, and filter contextual facts across sessions.
• Thought tracking that logs the internal reasoning steps of the model, facilitating debugging and auditability.
• Visual loading animations that improve user experience in terminal or web interfaces.

Real‑world scenarios benefit from Saqr-MCP’s modularity. A knowledge‑base bot can fetch up‑to‑date policy changes via web search, store them in mem0 for future reference, and generate compliance reports as Word documents—all orchestrated by a single MCP client. In research pipelines, developers can blend local LLMs for privacy‑sensitive data with Groq’s high‑throughput inference, while still leveraging the same toolset for citation generation and summarization. The server’s async architecture ensures that these operations scale without blocking the main conversation thread.

For developers familiar with MCP, Saqr-MCP stands out by bundling a full suite of practical tools into one server. It eliminates the need for separate wrappers around each service, streamlines integration with AI assistants, and provides a clean, extensible foundation for building custom workflows that combine inference, search, memory, and document creation in a single, coherent pipeline.