Catalysis Hub

The Catalysis Hub MCP server bridges the gap between advanced AI assistants and a rich, research‑grade database of catalytic phenomena. By exposing Catalysis Hub’s GraphQL API through the Model Context Protocol, it gives developers a single, consistent interface to query reactions, materials, publications, and surface‑reaction data without worrying about authentication or API limits. This is particularly valuable for scientific workflows that rely on up‑to‑date, high‑fidelity data to train models or generate hypotheses.

At its core, the server implements standard MCP transport over stdin/stdout and offers a direct GraphQL access endpoint. Users can submit any valid GraphQL query, optionally with variables, and receive a JSON payload that mirrors the native API response. This flexibility means developers can craft highly specific queries—filtering by temperature, catalyst composition, or material descriptor—while still benefiting from the MCP’s lightweight, language‑agnostic messaging format. Robust error handling ensures that connectivity issues or malformed queries are surfaced clearly, allowing AI agents to react gracefully.

Key capabilities include comprehensive data coverage (reactions, material systems, publications, surface reactions), variable‑parameterized queries for dynamic data retrieval, and compliance with the MCP specification to guarantee seamless integration with Claude or other agents. The server’s design also supports future extensions: adding new GraphQL endpoints or custom transformations can be done without altering the MCP contract, keeping the interface stable for consuming agents.

Typical use cases span academic research and industrial R&D. A researcher can ask an AI assistant to “find all reactions involving platinum at 300 K” and receive a precise list, while an engineer can query “materials with low activation energy for CO₂ reduction” to seed a machine‑learning model. In teaching, students can explore the database through conversational prompts, making complex datasets accessible without deep knowledge of GraphQL.

What sets this MCP apart is its direct, unmediated access to a domain‑specific knowledge base coupled with the simplicity of MCP. Developers can embed catalytic data queries into larger AI pipelines—such as automated literature review, property prediction, or synthesis planning—without wrestling with REST endpoints or SDKs. The result is a streamlined workflow where AI agents can fetch, analyze, and act on cutting‑edge catalysis research data in real time.