"Generating PowerPoint slides from Power BI datasets in folders" matching MCP servers:

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

A personal MCP bridge that lets ChatGPT drive a local Codex CLI, translating instructions into Codex app-server threads and returning compact summaries of what Codex did.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

An intelligent MCP server that analyzes projects and recommends professional role assignments, enabling users to download specialized agent templates for 100+ roles across various company departments.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Enables Claude Code to control Firefox through tools for tabs, navigation, clicks, typing, scrolling, screenshots, page reading, forms, JavaScript, and console/network logs.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.

Enables local opencode agents to control a live Chrome browser via MCP tools, including tab management, JavaScript execution, clicking, form filling, page reading, screenshots, and console log retrieval.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Enables autonomous agents and MCP-compatible clients to enforce policy-approved actions using risk tiers, reversible checkpoints, and token approval bounds.

Enables AI agents to manage live Firefox tabs and native tab groups via MCP tools over stdio, with exact-match verification and safety constraints. It does not read page content or send browser data remotely.

Enables Claude Code, Codex, Devin, or any MCP client to control Chrome/Edge through the repository's browser extensions, with Claude-style visual tools or Codex-style text-first tools.

MCP server that lets Claude control multiple persistent browser tabs inside VS Code, with tools for navigation, clicking, typing, snapshotting pages, and reading console output.

Enables AI agents to perform professional-grade deep research by aggregating real-time data from multiple sources, evaluating source credibility, and generating comprehensive reports.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

MCP server for FlashID client that lets AI agents manage browser profiles, proxies, and folders, and control browser automation endpoints via natural language.

A universal MCP server that loads Markdown skills from arbitrary folders, enabling lazy discovery and execution of skills across multiple MCP-capable tools.

Enables Claude Code to delegate prompts, reviews, and file-changing tasks to other coding agents such as Codex, Antigravity, Claude, Aider, and Goose, running each job in an isolated git worktree whose diff can be inspected before applying or discarding. All traffic passes through one policy layer with trusted folders, permission modes, minimal environments, timeouts, quota-aware fallback, and a hash-chained audit log.

Enables AI assistants to interact with the Apify platform to manage actors, monitor runs, and retrieve scraped data from datasets. It supports natural language commands for executing web scrapers, managing tasks, and accessing key-value stores.