"Understanding Inference Models" matching MCP servers:

Renders OHLCV data as candlestick chart PNGs and returns inline images. It is a pure renderer with no network access, allowing models to view charts generated from provided data.

MCP server that enables natural-language-driven, headless HEC-RAS modeling, letting agents inspect, edit, run, compare, and plot 1D/2D hydraulic models without the GUI.

A Model Context Protocol (MCP) server that exposes tools for exploring and querying SQLite and PostgreSQL databases. It allows Large Language Models (LLMs) to inspect table structures, perform statistical profiling, generate entity-relationship diagrams, and execute queries safely.

Exposes NeuroGraph's scientific API to AI models as MCP tools, enabling them to query, analyze, and visualize brain networks — regions, tracts, connectivity, literature, and cross-species homologies — drawn from a portable SSD-based neuroimaging data library. Lets models read real atlas data, load datasets, compute graph metrics, and produce visualization scene descriptors without acting as the scientific engine itself.

Enables discovery of Fabric/Power BI workspaces and semantic models, execution of DAX queries, and generation of executive HTML reports with visualizations.

Provides AI models with structured design guidelines and system prompts for creating consistent, high-quality interactive visualizations like charts, diagrams, and mockups. It enables on-demand loading of UI specifications to optimize token usage while ensuring visually polished and functional widget generation.

Enables AI models to generate interactive visuals including charts, diagrams, UI mockups, and SVG graphics from plain text prompts. This Windows-based MCP server serves as an intermediary to bridge AI tools with visual output capabilities.

Enables AI agents to author and edit Power BI files locally (.pbix/PBIP), covering semantic models, reports, and Power Query M with 490 tools.

Enterprise-grade MCP server for Sigma Computing, providing 155 tools for managing workbooks, data models, members, teams, deployments, webhooks, and multi-tenant operations. Enables AI assistants to interact with Sigma via natural language.

Creates, inspects, validates, and modifies Power BI Project (.pbip) folders, generating PBIR-style reports and TMDL semantic models from structured inputs.

Enables AI assistants to create, edit, and export IFC5/IFCX building information models through natural language, handling spatial structure, elements, geometry, metadata, validation, and export.

Enables ChatGPT and Claude conversations to display accessible interactive charts and lets models navigate readers through data points using speech, braille, and sound.

An MCP server that generates and manages nifdi diagrams as SVG files, bundled with skills that teach models in depth, placement grammar, and build workflow through natural language.

An MCP server that lets an AI assistant drive Epistemic Network Analysis models hosted on the webENA cloud service, enabling project listing, model running, and result visualization.

Converts plain-English stock requests (e.g. "compare Apple and Microsoft YTD") into validated yfinance/matplotlib scripts, AST-checked and executed in a sandbox to produce chart PNGs. Exposes parsing, saving, execution, and history-listing tools to Claude Desktop and Claude Code through FastMCP, backed by local Ollama/DeepSeek-R1 or cloud Groq/GPT-4o models.

Enables AI agents to securely connect to your own Power BI or Fabric tenant using your Azure identity, with tools to inspect workspaces and models, run read-only DAX, validate and deploy your own reports, take page screenshots, and enforce guardrails that protect shared resources.

Enables MCP clients to watch and drive a running ResInsight, query reservoir simulation models, and render pictures and plots without touching the GUI.

Enables LLMs to interact with Sisense data models and create charts programmatically via natural language, supporting tools for data sources, fields, and chart building.

Provides quantitative analytics and statistical inference for financial data, including Monte Carlo DCF valuation, risk metrics, and trend regression diagnostics, enabling natural language-driven financial analysis via Claude Desktop.

Enables a local assistant to drive a full data workbench: ingest messy files, clean them with versioned and undoable steps, enforce quality rules, build charts and dashboards as code, train quick models, and ask questions of the data—all on the user's own machine.