"How to deploy Solana applications" matching MCP servers:

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

ORQIS MCP provides read-only tools for exploring workspaces, use cases, agents, shared memory, and temporal knowledge in the ORQIS AI Workspace.

MCP server that connects AI agents to Google NotebookLM, enabling natural language interaction with notebooks, including Q&A, source ingestion, and audio overview generation.

Enables local-first knowledge management for Codex, providing tools for guided discussions, contextual recall, review, and daily topics.

Builds a searchable memory bank from persona-specific PDF content and exposes it as an MCP tool for querying.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

Graph-based MCP server for persistent AI memory, session checkpointing, context compression, and cross-session context management for LLM applications.

Enables AI applications to add, search, update, and delete long-term memories using the Mem0 Memory API, allowing agents to persistently remember user preferences, conversation history, and contextual information across sessions.

A codebase ontology and a cited wiki, both kept as Markdown in one folder on your disk. Typed graph answers — what a change affects, how two concepts connect, which wiki lines lack a citation — and every write lands as a Git diff a person reviews.

A neurosymbolic AI server combining Prolog’s symbolic reasoning with Model Context Protocol (MCP) for hybrid AI applications.

Integrates with Red Hat APIs to enable searching Knowledge Centered Support (KCS) solutions, retrieving solution details, searching support cases, and obtaining case details through LLM applications.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

Gives Claude persistent, local context about how you work, your weekly agenda, and shipped work, with automatic work logging and git integration—all stored privately on your machine.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables AI assistants and applications to interact with Airtable bases through a secure OAuth-authenticated MCP interface, providing complete API coverage for all Airtable operations.