"How to Create 2D Fighting Games in Godot 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.

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.

MCP server for querying a personal knowledge base using semantic RAG, powered by DeepSeek and local embeddings from Ollama.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

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.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

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.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

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

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

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.

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

A read-only Model Context Protocol server that exposes a semantic knowledge base to AI agents via 27 tools. It enables querying of documents and data integrated from sources like Notion, SharePoint, HubSpot, and Slack.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

An MCP server that enables natural language interaction with Google's Discovery Engine API, allowing users to search, recommend, and manage data through conversational interfaces.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Provides access to Godot engine documentation through a Retrieval-Augmented Generation (RAG) system using ChromaDB. It enables users to query processed Godot documentation and technical chunks for development assistance within MCP-compatible environments.