"Information or resources related to scrap materials or processes" matching MCP servers:

Provides computed (DFT) materials structures and thermodynamic properties through the Pipeworx MCP gateway, enabling AI agents to query materials data via natural language or tool calls.

MCP server for materials science monitoring, searching ArXiv preprints and OpenAlex articles with sovereign intelligence filtering by country.

Provides AI assistants with access to materials science databases, enabling search and analysis of material properties, crystal structures, phase diagrams, and elastic properties through the Materials Project API.

Exposes materials-science data, simulations, workflows, and computing capabilities to MCP hosts through a plugin-agnostic, contract-governed engine with progressive capability discovery, bounded context, scoped permissions, and provenance-traceable artifacts. Concrete integrations such as a real OPTIMADE provider plugin stay separately packaged, so servers remain focused and evidence-backed rather than unrestricted.

An MCP server that provides governed metric definitions and provenance-labeled memory for materials test labs, enforcing quality system discipline on agent interactions.

HandaaS的MCP服务器，提供全域资讯搜索、企业新闻、行业资讯、主题跟踪和企业动态监控能力，帮助用户开展行业研究和舆情分析。

MCP server for BIOVIA Materials Studio 2023 on Windows, providing controlled preparation of Forcite and CASTEP inputs, model building, and LAMMPS/VMD pre-processing with reproducible, auditable workflows and strict scientific boundaries.

The Materials Project MCP Server is server that provides programmatic access to the Materials Project database. It enables LLMs, to search, analyze, and retrieve up to date materials science data.

Enables querying and retrieving materials data from the Materials Project via MCP tools, supporting search, lookup, comparison, and export.

Enables MCP clients to run materials computation workflows through tools for loading structures, relaxing potentials, screening doped variants, analyzing phonons and elasticity, sampling candidates, and replaying trajectories. It exposes these capabilities over stdio or streamable HTTP, adapting available engines and structure sources to the environment.

Enables AI agents to operate DSpace repositories through a typed interface, including SAF package building, metadata editing, scientometric harvesting, open-access PDF downloads, and repository administration.

A Model Context Protocol server that provides access to materials databases through the OPTIMADE API, with focus on Google DeepMind's GNoME dataset containing millions of predicted crystal structures.

An educational MCP server that wraps the Materials Project API, exposing tools to search chemical systems, get material summaries, find semiconductors by band gap, and retrieve crystal structure facts.

A tool for querying and analyzing materials data from the Materials Project database using natural language prompts, enabling materials scientists to explore properties, structures, and compositions of materials through conversational interfaces.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Converts PDF files to text for use with MCP-compatible applications like Cursor IDE.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

Provides access to Canadian federal parliamentary data (debates, bills, MPs, votes, Hansard transcripts) and legal information (case law and legislation through CanLII) for research and analysis.

Self-hosted MCP toolkit providing web search, Wikipedia, and arXiv access without mandatory API keys, delivered as a Docker Compose bundle.

This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.