"Creating custom experimental images in Docker" matching MCP servers:

A customized MCP memory server that enables creation and management of a knowledge graph with features like custom memory paths and timestamping for capturing interactions via language models.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

A custom MCP server that connects to self-hosted Mem0 API instances, enabling Claude Code to manage memories via custom endpoints.

A Model Context Protocol server that enables LLMs to perform web searches using Google's Custom Search API through a standardized interface.

A Model Context Protocol server that transforms text into structured JSON data using templates with placeholders.

Enables notes management, weather and news search, document ingestion and RAG-based semantic search using GroundX, with OpenAI GPT integration for summarization and completions.

A high-performance MCP server that connects tools like Cursor to Elasticsearch clusters using public-key authorization instead of API keys. It provides optimized tools for searching data, listing indices, and inspecting cluster mappings or shards with support for concurrent requests.

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

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 optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.

A domain-specific MCP server that provides optimized API access to the RAGmonsters fictional monster dataset, enabling more efficient and secure interactions compared to generic SQL queries.

Provides custom prompts for Amazon Q by serving markdown files from the local filesystem as prompt templates through the Model Context Protocol.

A Model Context Protocol server that bridges MCP clients with local LLM services, enabling seamless integration with MCP-compatible applications through standard tools like chat completion, model listing, and health checks.

Enables AI agents to search, fetch, and clean live documentation from LangChain, LlamaIndex, and OpenAI via Model Context Protocol, with token-optimized extraction.

MCP server that enables AI assistants to interact with the MemOS API, providing memory search, feedback management, and knowledge base operations via MCP-compliant tools.

An MCP server that connects AI assistants to a Neo4j graph database, enabling querying of movie data through tools like graph statistics, genre-based search, and movie details, along with advanced features such as LLM sampling and completions.

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

A Model Context Protocol server that provides access to Observe API functionality, enabling LLMs to execute OPAL queries, manage datasets/monitors, and leverage vector search for documentation and troubleshooting runbooks.

Processes documents, analyzes GitHub repositories, and researches companies using local Gemma3 AI to extract structured business insights for generating compelling case studies.