"Artifact Hub" matching MCP servers:

Enables local forensic analysis of files by orchestrating system binaries (file, exiftool, strings, Volatility) via safe subprocess execution.

A cross-AI-agent local task context sharing MCP server that allows clients like Codex, Claude Code, Hermes, and VS Code to read and write the same task state, enabling task tracking and context distillation.

The artifact store for AI agents. Every output your agents produce — persisted, retrievable, shareable. Across runs, sessions, and tools. Session/agent metadata, content-hash dedup, and expiring share links; available on npm (@artifacta-mcp/mcp) and PyPI (artifacta-mcp).

A TypeScript MCP server for financial data aggregation that connects AI assistants to SEC EDGAR filings, XBRL statements, FRED economic indicators, and real-time market data.

DC Hub is the live Model Context Protocol layer for data-center and energy infrastructure. AI agents call 92 tools across 24,600+ facilities for land+power, grid, interconnection, fiber, and Capacity Source — with as_of timestamps. Connect: https://dchub.cloud/connect · Docs: https://dchub.cloud/ai-agents · Endpoint: https://dchub.cloud/mcp · Pro $99/mo.

MCP server for AI agents providing 300 curated Japanese home & lifestyle products across 31 categories. Features mm-precision dimension search, related-item chains (1 product → 3-5 accessories), shelf+storage coordination, and Rakuten/Amazon affiliate links.

Security gate that scans MCP servers and Claude Code Skills for vulnerabilities before execution.

Self-hosted MCP + REST continuity hub for coding agents. Tracks unfinished work, progress, overlap, reminders, and project context across Cursor, Codex, Claude Code, and other MCP clients.

A multi-domain MCP server that exposes a small, stable surface of three tools (list_domains, list_domain_tools, call_domain_tool) to gate access to various tool domains like Plane and BloodBank, preventing agent schema overload.

Provides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.

An MCP server for aggregating multi-domain intelligence feeds, performing 80:20 crossover analysis, and publishing structured insights to multiple channels.

A unified MCP server that aggregates 32 sports API providers into a single service, providing 336 tools for scores, stats, odds, esports, and more across 70+ sports.

Provides a unified interface for autonomous AI agents to access 14 developer toolchains covering commerce audits, security guards, swarm coordination, machine trust, proof ledgers, crawling, telemetry, pricing, local model routing, micropayments, sandbox isolation, A2UI rendering, and message bus dispatch.

MCP server enabling AI agents to trace and resolve order synchronization incidents between an ERP (Odoo) and multiple marketplaces.

MCP server to manage agents, prompts, sessions, and metrics using strands-hub data stored in S3. Provides tools for listing agents, creating prompt versions, and querying run metrics.

Provides offline search and retrieval of curated scientific computing Agent Skills for modeling, computational chemistry, DFT, molecular dynamics, and visualization. It exposes tools for skill cards, compatibility checks, content/bundle retrieval, version diffs, health checks, and issue reporting via HTTP or stdio MCP.

An MCP server for autonomous quantum computing research that searches arXiv, ingests papers, generates ideas, runs and validates experiments, and produces reports.

An AI-native management hub that enables users to control WordPress, WooCommerce, and various self-hosted services like Gitea and Supabase through AI assistants. It provides a comprehensive suite of nearly 600 tools for managing content, ecommerce stores, repositories, and databases using natural language.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Enables AI agents to inspect and work with PDF files entirely on the local machine, covering page counts, metadata, layout, encryption permissions, scan-vs-text detection, and page rendering. Generated results land in a temporary workspace as short artifact ids that can be chained between tools and exported to real folders on request.