ASR Graph of Thoughts (GoT) MCP Server

Overview

The ASR Graph of Thoughts (GoT) MCP Server is a specialized Model Context Protocol implementation designed to bring graph‑based reasoning into AI workflows. Traditional text‑centric prompts often struggle with complex, multi‑step problem solving; this server addresses that gap by representing each inference as a node in a directed graph, allowing the assistant to decompose problems, generate intermediate hypotheses, gather evidence, prune irrelevant branches, and ultimately compose a coherent solution. The result is a more transparent, traceable reasoning process that developers can inspect, modify, and extend.

What makes this MCP valuable is its pipeline architecture. The server exposes a series of processing stages—initialization, decomposition, hypothesis generation, evidence collection, pruning, subgraph extraction, composition, and reflection—each encapsulated as a modular endpoint. An AI assistant can invoke these stages in sequence or selectively, enabling fine‑grained control over the reasoning flow. For developers building complex decision support systems or scientific research assistants, this modularity means that custom logic can be injected at any point without rewriting the core engine.

Key capabilities include:

• Graph Construction and Manipulation: Uses NetworkX to build, query, and visualize the reasoning graph, making it easy to audit intermediate steps.
• Stage‑wise API: Each stage is exposed via a FastAPI route, allowing asynchronous or synchronous invocation from any MCP‑compatible client.
• Adaptive Pruning and Reflection: The server automatically removes low‑confidence branches and performs a reflective review of the final graph, ensuring that only the most robust conclusions are presented.
• Docker‑Ready Deployment: A ready‑to‑run Docker Compose configuration bundles the backend and a static JavaScript client, simplifying integration into existing CI/CD pipelines.

Real‑world use cases span scientific research assistants that need to trace experimental logic, educational tools that illustrate stepwise problem solving, and enterprise decision engines that must justify recommendations with a clear lineage of evidence. By exposing the reasoning graph, stakeholders can audit decisions, detect bias, and retrain models on specific subgraphs.

Integrating the ASR GoT MCP into an AI workflow is straightforward: a Claude or similar assistant sends a prompt to the server, receives a graph ID, and then queries the intermediate stages or final composition as needed. The server’s adherence to MCP standards ensures that any compliant client can interact with it, while its extensible design allows developers to plug in custom heuristics or external data sources at any stage. This combination of transparency, modularity, and ease of integration gives the ASR Graph of Thoughts MCP a distinct advantage for building trustworthy, explainable AI systems.