"Keynote MCP (Model Context Protocol) Server" matching MCP servers:

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Local macOS MCP server that reads, edits, and exports Apple Keynote presentations via JXA. Enables Claude to visually analyze slides, apply design changes, and iterate on presentations directly.

Apple keynote mcp for local editing

A Model Context Protocol server that enables AI assistants to control Keynote presentations through AppleScript automation, supporting slide operations, theme-aware content management, and export functions.

A Model Context Protocol server that enables AI assistants to control Apple Keynote presentations through AppleScript automation, supporting comprehensive slide creation, management, and content operations.

Summarizes Slack activity and GitLab merge requests with OpenAI to help generate daily standups, weekly reports, or quarterly check-ins.

Enables harvesting assets from Apple Keynote decks, including PDF export, previews, extractable text, embedded images, and manifests through MCP resources and tools.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

An MCP server that integrates AI retrievals with NASA's Common Metadata Repository (CMR), allowing users to search NASA's catalog of Earth science datasets through natural language queries.

A server that allows users to query and analyze cloud cost data from Cloudzero using natural language through Large Language Models.

Discovers local model runtimes such as Ollama, LM Studio, and LocalAI, and exposes their endpoint metadata to MCP clients like Copilot.

Enables AI coding agents to offload large file reads and boilerplate code generation to cheaper or local worker models through MCP, reducing token consumption and keeping context clean.

Enables AI agents to programmatically create, inspect, edit, validate, and headlessly render traditional 2D hand-drawn animation projects, including frames, layers, audio, camera keyframes, spritesheets, and video or GIF exports.

Enables AI assistants to directly manage Anki flashcards, decks, notetypes, tags, and media, with support for batch operations, searches, and deck exports via file-based input/output.

Provides full programmatic control over Apple Keynote through the Model Context Protocol, enabling creation, editing, presenting, and exporting of presentations via natural language.

A comprehensive MCP server for managing AI-assisted Dungeons & Dragons campaigns, featuring tools for character sheets, combat tracking, and world-building. It enables players and DMs to interact with 5e game mechanics and query personal PDF rulebooks using RAG capabilities.

Enables language models to receive external randomness so the same prompt can produce different answers while still following the user's request.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

Routes subagent tasks across Claude and OpenAI lanes by task kind and quota state, deciding whether to spawn a single model, run a duel of both vendors, or run both and ship the merge. It records duels with attested proof-of-run session ids, grades sides blind with one judge per vendor, and tracks per-kind standings and lane pacing.

Enables multi-agent coordination with file locking, shared registry, async messaging, and architectural decision logging via MCP tools.

This MCP server provides a local-first control plane for coding agents, enabling task routing, contract management, and shared learning across multiple AI vendors via markdown files.

MCP server for discovering and cataloging generative AI models, enabling agents to search by modality, view pricing/terms, and manage contracts with providers via Beckn-aligned APIs.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Proxy for OpenAI-compatible backends that compacts conversation history before the context window fills, with read-only MCP tools to search and read back the archived originals.

Enables users to search locally across imported Claude, ChatGPT, and Claude Code conversations and retrieve surrounding turns with citations, while keeping indexing and storage on their own computer.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Provides official INDEC register designs and methodological rules to AI models, enabling accurate EPH data analysis code (R/Python) without hallucinations.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

Deterministic dependency + CVE context for AI coding tools, over the Model Context Protocol. A ~0.85 MB pure-Rust MCP server.