"An in-memory data store for caching and message brokering" matching MCP servers:

Enables authenticated, explicit-text message exchange between independent clients through a durable queue with idempotent sends, replay-safe inbox reads, bounded conversations, pause/reset controls, and client-driven polling.

Commit Message AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

A Shopify UCP MCP endpoint that lets assistants search live catalog inventory for lab-grown diamonds, build an anonymous cart, open an unpaid checkout, and hand the buyer a link to pay. It also exposes eight diamond education tools and profile-injection for inventory lookup through a hosted helper.

Single MCP server that manages multiple personal collections (todos, bookmarks, etc.) with only six generic tools, driven by a YAML registry for validation.

Enables independently operated AI clients to exchange private 1:1 messages and files using authenticated participant identities, with retry deduplication, status tracking, notifications, and owner conversation inspection. Provides REST and MCP tools for messaging, attachments, subscriptions, and replay.

An MCP server that scans code repositories for app store rejection risks by mapping API usage to required declarations and policy deadlines, enabling agents to catch issues before submission.

An MCP server that connects to the Google Play Developer API to deploy apps, manage releases, respond to reviews, and monitor app health through an AI assistant.

Enables persistent skill management over the Model Context Protocol, allowing agents to use, write, list, and migrate skills backed by directory, Git, or S3 stores.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Provides Claude Co-work with MCP tools to search and record organizational memory, including search, read, query, ingest, remember, sync, and status operations.

MCP server providing durable Substrate organizational memory tools, including search, read, query, ingest, remember, and sync. It integrates with Codex to automatically capture completed turns and session boundaries for persistent memory.

An MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.

Enables Claude to read and manage App Store listings, builds, TestFlight testers, localized store texts, screenshots, and customer reviews through Apple's official App Store Connect API, with read-only access by default and explicit approval for each change.

Enables LLM applications and agents to persist and recall scoped memories in a local SQLite store, with semantic/hybrid/metadata search, review-first corrections and summaries, and controlled export, backup, and purge operations over stdio.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Aggregates multiple MCP servers and custom Python tools behind a single endpoint, with intelligent context and tool discovery for AI agents.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe an LLM fine-tuning job in natural language and receive ranked GPU recommendations from a dozen cloud providers, based on estimated VRAM and FLOPs requirements. It surfaces the cheapest, fastest, and most balanced hardware options while disclosing the source and freshness of each price.

Enables users to describe an LLM fine-tuning workload once and receive ranked cloud GPU recommendations with estimated VRAM/FLOPs requirements, cost, runtime, and data-source transparency.