"Blender Scene Generation and Importing into Unreal Engine" matching MCP servers:

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

Production HTTP 402 micropayment web scraper, Llama-3 digest, and Twitter intelligence engine for AI agents on Base L2. Zero API keys, zero subscriptions.

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 MCP server that enables generating scripts based on specified topics and keywords, while also providing functionality to store and summarize notes.

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.

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 Model Context Protocol server that distills documents from multiple sources into HTML articles and Obsidian notes, with features like key element detection, image filtering, and async task management.

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.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.

Turns plain text or Markdown, web pages, and local PDFs into editable Markdown outlines that can be rendered as standalone, offline-capable interactive HTML mind maps. Works with any MCP client with no API key required, since the connected model writes the final outline from a draft and cleaned source text.

Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.

MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.

An MCP server that enables searching arXiv, importing papers, enriching with citation data, generating summaries and research ideas, and querying a local library using local LLMs and embeddings.

Enables agents to parse resumes and job postings into structured OTP/OJP skeletons, validate them against JSON Schema, and extract agent-friendly summaries, all without LLM calls.

A single MCP server that merges three context-engineering ideas into one toolset for AI agents

Enables everyday utilities like calculation, text analysis, encoding, hashing, UUID generation, datetime lookup, and note management through MCP tools, resources, and prompts.

MCP server that compresses markdown files into caveman-style prose, cutting token usage by 65% while preserving code and structure.

Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.

MCP server that converts video links into AI-generated Markdown notes, with tools for task management, transcription engines, and LLM providers.

Enables LLM agents to search arXiv, download papers, parse PDFs into structured sections, and extract key findings using client-side LLMs. Features persistent caching and layout-aware PDF extraction.