"MCP server for finding research data and models for AI/ML training" matching MCP servers:

Provides machine learning researchers with tools for creating publication-quality scientific visualizations, statistical plots, and 2D data representations. It streamlines the research workflow by enabling AI assistants to generate complex figures from CSV, JSON, or direct data inputs.

A CDP-native MCP server that lets AI agents drive a real Chrome browser with deep network/console inspection, robust page interaction, and the ability to reverse-engineer a site's API flow into runnable code with dependency chaining and replay verification.

MCP server that launches Mesen/MesenCE headlessly and exposes the emulator's debugger-face Lua API over MCP JSON-RPC, enabling AI agents to load ROMs, step frames, inspect memory/registers, manage breakpoints/watches, trace execution, and export Code/Data Logger maps for SNES, NES, PC Engine, and Game Boy Advance.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

An MCP server that wraps the @apitomy/data-models library for querying, validating, and editing OpenAPI and AsyncAPI documents.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

Enables AI agents to manage n8n workflow automation instances through tools for workflow CRUD operations, execution monitoring, and webhook triggering. It facilitates programmatic interaction with n8n instances via the n8n API with AI-optimized descriptions and error handling.

Lets a coding agent hand a plain-language app idea to a hosted AI software team that shapes, designs, builds, tests and deploys it, returning a one-time link into the full build workspace. Also lists your apps with their stage, pending approvals and live addresses after a standard OAuth sign-in.

Enables searching, comparing, and inspecting AI models by pricing, context window, and capabilities via the models.dev catalog.

Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.

Classifies development task complexity (LIGHT/MEDIUM/HEAVY) and recommends the most cost-efficient AI model per provider, enabling optimized model selection for coding tasks.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

Transforms AI assistants into a full ML engineering environment for training and fine-tuning models across multiple backends (local GPU, Mistral, Together AI, OpenAI) and cloud providers (Lambda Labs, RunPod, SSH-accessible VPS), with dataset management, experiment tracking, cost estimation, and deployment to Ollama/Open WebUI.

Enables OpenAI-compatible clients to access external tools, APIs, repositories, and data sources through MCP with allowlisting, approval, and argument validation, deployable as a FastAPI service or Python library.

Enables MCP-compatible AI clients to inspect Godot project and editor state, observe opted-in running scenes, manage processes, and apply permission-scoped guarded edits. It starts read-only and exposes additional capabilities only through explicit command-line flags.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

Enables Codex to consult Claude Code for a second opinion through a local stdio MCP server, exposing the ask_claude tool for text-only reviews and recommendations using existing Claude Code authentication. Codex can request findings and tradeoff analysis without allowing Claude to edit files or run commands.

Enables AI coding agents to discover and verify current open-source libraries and models using live GitHub activity, PyPI adoption, license checks, and hardware VRAM estimates before generating code.

A live MCP server that connects AI assistants to a running Obsidian instance, enabling full app automation including reading notes, clicking UI elements, managing plugins, and executing JavaScript.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Enables Codex and other MCP clients to read kickoff cards, acceptance criteria, Git evidence, and next steps, then append validated, audited progress events for resumable project handoffs.

Enables agents to read a project's kickoff card, acceptance criteria, AGENTS rules, handoff package, progress, Git trail, and next step, and to append validated, audited progress events with matching Git commits through short-lived scoped credentials.

Enables AI agents to retrieve Git-backed project context, handoff packages, progress, and next steps, and to append validated progress events with matching project-record commits. This supports visible agent progress, resumable work, and safe handoffs while keeping high-risk actions outside the MCP surface.

A local, offline MCP server that enables AI agents to collaborate with SketchUp for controlled modeling, editing, and review tasks via 41 MCP tools.

Enables agents to read Git-backed project context and append audited, idempotent progress events through scoped, short-lived credentials, supporting resumable handoffs and visible project state.

Enables AI agents and users to verify claims about Jira issues and issue counts against live Jira, retrieve verifiable facts for drafting updates, and re-check previously posted claims. It provides read-only tools and an issue panel so status updates can be evidence-based and mismatches corrected before or after posting.

Enables Codex sessions to read a project's kickoff card, acceptance criteria, handoff package, progress, Git trail, and next step, then append validated, idempotent progress events backed by matching project-record Git commits. Access is limited to a short-lived, revocable credential scoped to one workspace and project, with high-risk actions kept outside the server.

Enables AI agents to read Git-backed project context and append validated, audited, idempotent progress events through short-lived, scoped credentials, supporting resumable work and safer handoffs.