"Optical Character Recognition (OCR) Technology" matching MCP servers:

Enables AI assistants to access and update personalized context data, creating persistent memory between sessions.

Hosted MCP gateway to the RTP global commons of neighborhood practice and relational tech knowledge. AI builders can query 275+ recipes, frameworks, and methodology docs for community organizing.

An MCP server that provides AI tools with relational tech principles, patterns, and the Studio library to guide builders in creating community-centered tools using embedded design.

An MCP server for reading, creating, and managing PDF, DOCX, and Excel documents with OCR, styling, categorization, and remote file bridges.

Enables AI hosts to plan, draft, and check long-form novel chapters while honoring canon settings, character state, arcs, and check ordering, with approval-gated atomic commits. It keeps world, character, and chapter files as the human-editable source of truth, with snapshots and rollback for recovery.

Enables reading and updating relationship data (likes, dislikes, important info, feelings, closeness, notes) for characters in a multi-character system, supporting both private and public relations.

Enables Claude AI to seamlessly interact with Scrivener projects for document management, AI-powered content analysis, and advanced writing assistance.

Enables AI assistants to search and retrieve encrypted local screen history, including OCR text, recent activity, context around moments, daily rollups, and screen snapshots.

Provides a shared knowledge base MCP server for coding agents to read and leave one-sentence lessons, tagged by technology stack, to avoid repeated mistakes across sessions.

Enables persistent, model-driven text adventures with transactional saves, character knowledge boundaries, cited canon retrieval, dice outcomes, and forkable historical timelines via MCP tools and local SQLite storage.

Enables AI clients and agents to turn a new idea into a structured set of clarifying questions, technology options, upward design breakdowns, reverse reasoning, and risk assessments so users can steer top-level design decisions. It also supports saving insights and building a bidirectional design tree of concepts and their implementations.

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 chatting with an AI waifu character through MCP tools, with user management and dialog history.

Local MCP server for Call of Cthulhu 7e, managing campaigns, character knowledge, and rule resolutions (d100, SAN, combat) with secure state isolation and keeper-controlled access.

Connects AI assistants to your Argo campaigns via the Model Context Protocol. Once configured, your AI assistant can read and write campaign lore, look up character details, and interact with Argo data directly from the chat interface.

Enables a scriptwriting agent to build and query a persistent, per-project ledger of story propositions and each character's knowledge state, so every proposed line of dialogue is checked before it is written and blocked when it uses information that character cannot yet know. Also supports full-script leak audits, incremental scene ingestion, wiki-style ledger lookups, and waiving false positives with an auditable decision trail.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with persistent storage and semantic similarity matching.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.