"Unreal Engine" matching MCP servers:

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

A persistent world your agents share across sessions and models. MCP memory server with semantic memory retrieval, cross-agent handoff, a hosted remote endpoint with OAuth, a free 24-hour room, and an open-source local stdio server.

Provides AI models with structured access to Trino's distributed SQL query engine, enabling LLMs to directly query and analyze data stored in Trino databases.

An ultra-rational A2A protocol for zero-token edge pre-filtering and FEP-driven deadlock prevention. Uses Cloudflare Vectorize (384d cosine similarity) with a 24h deposit model, restricting bargaining to a 4-rally limit before forcing HTTP 402 dimension jumps.

Entroly is a high-performance context engineering engine that optimizes AI coding agent context windows through information-theoretic compression and reinforcement learning. It gives your AI 100% codebase visibility while reducing token consumption by an average of 78%.It's learns fully autonomously.

Long-term persistent memory for AI agents with hybrid search (DiskANN + FTS), bi-temporal knowledge graph, and 81.1% LongMemEval accuracy.

A semantic layer query engine with MCP support, enabling AI assistants to query structured data through natural language and declarative interfaces.

Unified real-time search engine skill for AI agents.

Graph RAG based context management engine for coding agents

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

Enables AI coding agents to query repository structure, symbols, dependencies, Git history, architecture, memory, impact, and guarded edits through a stable MCP interface, returning ranked task-specific evidence within a context budget.

A RAG engine and MCP server that indexes .md files to provide searchable context to AI agents via the Model Context Protocol.

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.

Local context management, search engine, and memory for agentic AI via MCP, enabling efficient context retrieval and storage.

MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.

A narrative graph engine that enables LLMs to generate, track, and mutate complex fictional worlds while maintaining consistency between factions, characters, and locations. It acts as a specialized RAG framework for storytelling, allowing models to manage thousands of entities without exceeding context limits.

Multi-engine aggregated search MCP server that combines results from 7 search engines with deduplication, relevance ranking, and web page content extraction.

Self-improving documentation engine that ingests Slack and GitHub PRs to detect knowledge gaps and improve documentation automatically.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.