"Creating and Editing PowerPoint Presentations" matching MCP servers:

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

Provides structured dealer brand data including inventory, promotions, reviews, and dealer profile via MCP tools, enabling LLMs to answer accurate brand-related queries.

A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.

Converts PDF, Word, PowerPoint, Excel, and HTML documents into Markdown, handling files up to 8 MB in a single call and routing larger or scanned files through an OCR and table-detection queue that returns Markdown plus a JSON structure file. Results arrive either by polling or a signed webhook.

Enables AI assistants to interact with Tencent Lexiang enterprise knowledge base for searching, reading, creating, and editing documents, managing files, and importing meeting recordings.

Enables vectorless RAG by letting LLM clients like Claude and Cursor parse documents, inspect outlines, and retrieve specific sections with heading breadcrumbs. Supports PDF, Word, HTML, and PowerPoint without LLM calls or heavy ML models.

Offline AI-powered local file search MCP server for Windows. Searches inside document contents (Word, Excel, PDF, PowerPoint, HWP) using BM25 + dense vector hybrid search. 100% local, no cloud, no login, no telemetry.

Upload documents (Word, Excel, PDF, PowerPoint) to a vector RAG store and perform semantic search with page-level citations. Queries are free; ingestion costs credits at break-even pricing.

Enables AI agents to inspect and convert PDF, PowerPoint, Excel, and many other file formats into clean, structured Markdown, with chunking support for long documents.

MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.

Upload Word, Excel, PDF, or PowerPoint documents to a vector RAG store with vision-model extraction, then search semantically and retrieve chunks with page numbers for precise citations.

Enables AI assistants to parse and search documents including PDF, Word, Excel, PowerPoint, and images via OCR, with support for semantic search and batch processing.

Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.

RAG-powered document search server that enables semantic search across large collections of legal and business documents (PDF, Word, Excel, PowerPoint) using local embeddings with no API costs.

Provides read-only MCP tools to find, list, and retrieve sales case studies with hybrid search, returning complete cases and prepared presentations.

An MCP server that provides targeted PostgreSQL schema access to AI agents, retrieving only relevant tables for a question and enforcing read-only access through multiple layers.

The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.

Enables saving project facts as they occur and retrieving only task-relevant entries when starting new work, using Jev evaluation to rank them while preserving all stored context.

Enables searching and reading AVEVA Customer Support Portal knowledge base articles, including tech notes, known issues, and error-message diagnostics, to ground AI answers in support content not found in public documentation.