A multi-agent RAG MCP server that answers questions from your documents with a human-in-the-loop approval step, using a coordinator, retriever, and synthesizer agents.
A local MCP server that lets any LLM agent manage Pine AI tasks — negotiate bills, cancel subscriptions, resolve disputes, and make phone calls on your behalf.
Enables AI agents and assistants to extract data from social media, search engines, maps, e-commerce sites, and any other website by running thousands of ready-made scrapers, crawlers, and automation tools as callable tools, with OAuth-based connection and optional agentic payments.
Enables AI assistants to perform defensive security tasks such as vulnerability detection, CVE lookup, phishing/link safety checks, and security report generation via MCP tools.
Enables AI agents to interact with Salesforce through MCP, supporting queries, records, metadata, and bulk operations with flexible OAuth authentication.
MCP server for DCI API enabling AI models to extract and analyze DCI jobs, components, and topics, with integrations for Google Drive, Jira, GitHub, and Red Hat support cases.
MCP server enabling AI agents to explore SAP HANA schemas, execute SQL with guardrails, and access database metadata, monitoring, and business domain semantics.
MCP server for managing a media server stack (Plex, Radarr, Overseerr, Bazarr, Prowlarr, Trakt.tv) using natural language to browse, request, and discover content.
Enables AI applications to write and execute natural language queries on organizational data stored in DX Data Cloud's Postgres database. Runs locally and provides a query tool for AI assistants to formulate and execute database queries with user approval.
Automates competitive research for startup ideas by executing a 7-step pipeline including idea understanding, competitor discovery, profile extraction, feature comparison, market gap analysis, innovation scoring, and report generation.
A template and demonstration project for building, testing, and deploying remote MCP servers using FastMCP and uv. It provides a foundational structure for creating MCP-compliant tools that can be hosted publicly and integrated with LLM agents.
A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.