A simple note-taking MCP server that allows storing and summarizing notes with custom URI schemes and provides functionality to add notes and generate summaries with different detail levels.
A TypeScript-based MCP server that facilitates note management and weather queries. It enables users to create, access, and summarize notes while providing integration for services like flomo.
Enables exploring Steam store reviews and community discussion threads with server-side filtering, temporal analysis, and metadata context for understanding player sentiment and feedback beyond store page noise.
An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.
A read-only, local MCP server that enables Claude Desktop and Claude Code to brief users on selected WhatsApp chats, surfacing mentions, urgent items, follow-ups, bills, and payment screenshots through local OCR while keeping data encrypted on the user's PC.
Enables MCP clients to call pay-per-use tools for search, PDF processing, web reading, extraction, summarization, fact-checking, translation, jobs, and discovery, with each call paid in USDC on Base via x402 and no API key or account required.
A local-first Codex plugin that bundles an MCP server to safely interact with Mattermost, enabling channel/thread summarization, conversation search, and reviewed post publishing via the Mattermost REST API.
Provides calendar, email, meeting transcription and summarization, and weather tools via MCP, enabling users to manage schedules, handle correspondence, capture meeting insights, and check conditions through natural language.
Enables Claude to read, rewrite, generate, and summarize LibreOffice Writer documents with preview-then-apply editing through MCP tools, authenticating via Claude Code login.
Self-hosted ML inference MCP server providing embeddings and summarization tools using sentence-transformers and Ollama, with low-latency preloaded models and API-key authentication.
Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.
MCP server for Differino that lets AI agents compare PDF, DOCX, and TXT documents, with tools for visual and text diffs, summaries, exports, and account balance checks.
A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.
MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.
tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.
Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.
Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.
A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.
Enables LLMs to interact with Fathom.video API for managing meeting recordings, retrieving transcripts and AI-generated summaries, searching meeting content, and accessing analytics and team data.
Integrates Vale prose linting into AI coding assistants, enabling users to check text files for style and grammar issues using Vale's powerful linting engine. Provides automated style feedback with smart configuration discovery and rich formatted results.