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At least 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗

NASA NTRS1991

High-dynamic-range MCP structures

We report on the development of a new high-dynamic-range two-stage Multi-Anode Microchannel Array (MAMA) imaging tube designed for improved high count rate performance at FUV and EUV wavelengths. The new two-stage MAMA tube employs two 25-mm-diameter format MCPs placed in tandem with a small gap between the plates. The front (input) MCP is designed to be a low-gain converter plate that supports an opaque photocathode and converts the detected photons to electrons, while the second (output) MCP is of higher conductivity and thus maintains the overall gain of the multiplier at high count rates. The second MCP is mounted in proximity focus with a (224 x 960)-pixel fine-fine coincidence MAMA array for high-spatial-resolution imaging studies. The applied voltage across each MCP can be controlled independently. We report on the gain and dynamic range performance characteristics of the two-stage MAMA tube in two different configurations: first, with the output MCP having moderate conductivity (about 100 MOhm); and second, with the output MCP having very high conductivity (about 2 MOhm). These results are compared and contrasted with those of the more conventional MAMA tube configuration which employs a single high-gain curved-channel MCP.

Slater, David C.↗

DOE OSTI2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.

AI↗

DOE OSTI2026

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

mada-tools: MCP servers, configurations, skills, and examples for MADA

MADA-tools (Multi-Agent Design Assistant tools) is a library for defining MCP (Model Context Protocol) servers that can be used by AI agents in the MADA project. Each MCP server provides a focused set of tools that enhances an LLM's knowledge and capabilities for a specific domain, for example, how to launch jobs with Flux versus Slurm. The library makes it easy to configure and start multiple MCP servers using configuration files or command line options. Once running, these servers are intended to be consumed by one or more agents in the MADA ecosystem. The system is designed to be extensible so that future projects can contribute their own MCP servers, skills, and toolsets.

Gunnarson, BrianS [Lawrence Livermore National Lab↗

DOE OSTI2025

EnergyPlus Model Context Protocol Server (EnergyPlus-MCP) v0.1

EnergyPlus-MCP is the first open-source Model Context Protocol server specifically designed for EnergyPlus building energy simulation. This innovative software enables AI assistants and other applications to interact programmatically with EnergyPlus through a standardized, secure interface, eliminating traditional technical barriers in building energy modeling. The software provides specialized tools across five functional domains: server management, model configuration and loading, comprehensive building component inspection, systematic model modification, and simulation execution with results visualization. Key features include automated HVAC system discovery and topology mapping, advanced schedule analysis, intelligent model validation, and interactive visualization capabilities. EnergyPlus-MCP's layered architecture ensures robust separation between protocol communication and domain expertise, enabling scalable deployment across organizations, educational institutions, and research teams. Unlike direct LLM approaches that suffer from inconsistent results and security gaps, EnergyPlus-MCP provides validated, reliable interactions while maintaining scientific rigor. This democratizes sophisticated building energy analysis, making EnergyPlus accessible to broader audiences through conversational interfaces and streamlined workflows.

Li, Han [Lawrence Berkeley National Laboratory (LB↗

DOE OSTI2026

OpenStudio®-MCP [SWR-26-035]

OpenStudio®-MCP is a Model Context Protocol (MCP) server that lets AI assistants perform building energy modeling through natural language. Rather than requiring users to learn the OpenStudio® SDK, EnergyPlus® scripting, or Ruby/Python automation, the server translates conversational requests into sequences of tool calls that create models, design HVAC systems, run simulations, and extract results — all within a single chat session. The server's 124 tools are organized into a skills architecture where each skill encapsulates a domain of building energy modeling (envelope, HVAC, loads, weather, simulation, results) behind typed, LLM-friendly interfaces. High-leverage operations like applying ASHRAE 90.1 baseline systems or generating standards-compliant typical buildings are exposed as single tool calls that internally wire dozens of OpenStudio® objects. Bundled measures from ComStock™ and Openstudio® -common-measures-gem are wrapped with dedicated tools and typed arguments rather than exposed through a generic measure interface, so AI models get consistent, error-resistant recipes without needing to discover measure arguments at runtime. A key design decision is structured results extraction: six SQL-based tools return surgical ~300–1,000 token responses (end-use breakdowns, envelope summaries, HVAC sizing, timeseries data) instead of requiring the AI to parse ~100K-token raw HTML reports, making iterative design exploration practical within context window limits. The codebase is designed as a reference implementation — explicit, well-commented, and modular — so that other simulation engines (EnergyPlus® standalone, TRNSYS, DOE-2) can use it as a template for building their own MCP servers.

Ball, Brian [National Laboratory of the Rockies (N↗

DOE OSTI2025

Napari-MCP

Napari-MCP connects the open source napari software to modern LLMs. It includes a socket server plugin for napari as well as the MCP tools.

Liu, Shusen [Lawrence Livermore National Laborator↗

NASA NTRS2004

Modular Engine Noise Component Prediction System (MCP) Program Users' Guide

This is a user's manual for Modular Engine Noise Component Prediction System (MCP). This computer code allows the user to predict turbofan engine noise estimates. The program is based on an empirical procedure that has evolved over many years at The Boeing Company. The data used to develop the procedure include both full-scale engine data and small-scale model data, and include testing done by Boeing, by the engine manufacturers, and by NASA. In order to generate a noise estimate, the user specifies the appropriate engine properties (including both geometry and performance parameters), the microphone locations, the atmospheric conditions, and certain data processing options. The version of the program described here allows the user to predict three components: inlet-radiated fan noise, aft-radiated fan noise, and jet noise. MCP predicts one-third octave band noise levels over the frequency range of 50 to 10,000 Hertz. It also calculates overall sound pressure levels and certain subjective noise metrics (e.g., perceived noise levels).

Golub, Robert A.↗

NASA NTRS2010

Software Model Checking of ARINC-653 Flight Code with MCP

The ARINC-653 standard defines a common interface for Integrated Modular Avionics (IMA) code. In particular, ARINC-653 Part 1 specifies a process- and partition-management API that is analogous to POSIX threads, but with certain extensions and restrictions intended to support the implementation of high reliability flight code. MCP is a software model checker, developed at NASA Ames, that provides capabilities for model checking C and C++ source code. In this paper, we present recent work aimed at implementing extensions to MCP that support ARINC-653, and we discuss the challenges and opportunities that consequentially arise. Providing support for ARINC-653 s time and space partitioning is nontrivial, though there are implicit benefits for partial order reduction possible as a consequence of the API s strict interprocess communication policy.

Thompson, Sarah J.↗

DOE OSTI2024

Advances in the Large Area Picosecond Photo-Detector (LAPPD TM ): 8" × 8" MCP-PMT with Capacitively Coupled Readout

Abstract We present advances made in the Large Area Picosecond Photodetector (LAPPD), an 8" × 8" microchannel plate photomultiplier tube (MCP-PMT), since pilot production was initiated at Incom, Inc. in 2018. The Gen-I LAPPD utilizes a stripline anode for direct charge readout. The novel Gen-II LAPPD employs an internal resistive thin-film which capacitively couples to a customizable external signal readout board, streamlining production and increasing customer flexibility. The Gen-II LAPPD, with an active area of 373 cm 2 , is capable of high single photoelectron (PE) gain of ∼10 7 , low dark rates (∼1 kHz/cm 2 ), single PE (SPE) timing resolution of ∼65 ps, and 𝒪(mm) position resolution. Coupled with a UV-grade fused silica window, the LAPPD features a high quantum efficiency (QE) bialkali photocathode of >30% at 365 nm with spectral response down to ∼165 nm. The LAPPD is an excellent candidate for electromagnetic calorimeter (ECAL) timing layers, photon-based neutrino detectors, high energy collider experiments, medical imaging systems, and nuclear non-proliferation applications.

Instruments & Instrumentation↗

DOE OSTI2025

Paraview-MCP

This project provides a streamlined way for users to interact with and control powerful scientific visualization software (ParaView) through a conversational interface. By developing an automated "Model Context Protocol" (MCP) server with a Python-based ParaView manager, the system allows users to seamlessly load and visualize complex datasets, explore visualization options with AI assistance, and optimize visualization output in a close loop. This is achieved by issuing intuitive, natural-language commands. The result is a user-friendly interface that integrates high-level conversation and scriptable data visualization, making scientific visualization tools more accessible to a broad audience.

Liu, Shusen [Lawrence Livermore National Laborator↗

DOE OSTI2026

AmeriFlux US-MCP Moro Cojo Slough, PG&E Treatment Wetland

This is the AmeriFlux version of the carbon flux data for the site US-MCP Moro Cojo Slough, PG&E Treatment Wetland. Site Description - Freshwater coastal wetland, restored in 2016, native grasses and sedges among freshwater ponds, surrounded 400m in each direction by agricultural plots, native plants remove excess agrcultural nitrogen from the soil

Paytan, Adina [University of California, Santa Cru↗

NASA NTRS1987

Curved channel MCP improvement program

Blowholes and blemishes were determined to start at two stages of manufacturing. Sperical blowholes resulted from trapped gas between the high melting temperature bond glass and the MCP wafer. During thermal processing, the trapped gas expanded and displaced the softened channel glass to form a spherical inclusion. This defect was eliminated by grinding the prefritted bond wafer and channel plate wafer to a flatness which ensured intimate contact prior to fusion. Elliptical blowholes or blemishes were introduced during the fiber draw stage. Contaminants trapped between the core bar and clad tubing volatized providing large quantities of expanding gas. These pockets of gas became elongated to an ellipsoidal shape during fiber draw. Special cleanliness procedures were developed for the grinding, polishing, and acid etching of core bars. Improvements in channel curvature fabrication were implemented. The design of the shearing fixture was evaluated. A new design was developed which eliminated an off-axis moment. The shearing furnace design was evaluated. Steady state thermal conditions instead of thermal transient conditions were determined to reduce curvature nonuniformity.

Laprade, Bruce N.↗

DOE OSTI2026

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗

DOE OSTI2025

GRAPH — an readout ASIC for large MCP based detectors

We present a programmable 16 channel, mixed signal, low power readout ASIC, having the project historically named Gigasample Recorder of Analog waveforms from a PHotodetector (GRAPH). It is designed to read large aperture single photon imaging detectors using micro channel plates for charge multiplication, and measuring the detector's response on crossed strips anodes to extrapolate the incoming photon position. Each channel consists of a fast, low power and low noise charge sensitive amplifier, which provides a myriad of coarse and fine programmable options for gain and shaping settings. Further, the amplified signal is recorded using, to our knowledge novel, the Hybrid Universal sampLing Architecture (HULA) ADC. A kind of mixed signal double buffer memory, that enables concurrent waveform recording, and selected event digitized data extraction. The sampling frequency is freely adjustable between few kHz up to 125 MHz, while the chip's internal digital memory holds a history 2048 samples for each channel, with a digital headroom of 12 bits. An optimized region of interest sample-read algorithm allows to extract the information just around the event pulse peak, while selecting the next event, thus substantially reducing the operational dead time. The chip is designed in 130 nm TSMC CMOS technology, and its power consumption is around 47 mW per channel.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

MCP agents in PanDA

Explore the source record for details and available documents.

97 MATHEMATICS AND COMPUTING↗