Using MCP Memory with GPT Actions

ChatGPT conversations are ephemeral by design. Even with conversation history, there is no structured way to retrieve a specific decision you made three weeks ago or a pattern you discovered across multiple sessions. Memory features exist but are limited and opaque.

GPT Actions bridge this gap by connecting ChatGPT to external APIs. A Custom GPT can call any authenticated endpoint, which means it can interact with the same Context Vault that serves your Claude Code and Cursor sessions.

The result is a single vault that works across all your AI tools. Save a decision in Claude Code, retrieve it in ChatGPT. Capture a pattern in Cursor, surface it in a Custom GPT. One memory layer, multiple clients, zero duplication.

Create a Custom GPT and configure it with a GPT Action pointing at your hosted Context Vault MCP endpoint. The action uses API key authentication — paste your Context Vault API key as the bearer token in the action auth settings.

Define three core actions that map to Context Vault tools: save_context for writing new entries, get_context for hybrid search and retrieval, and context_status for verifying the connection is healthy. Each action maps directly to the MCP tool schema.

Once configured, the Custom GPT can save and retrieve context using natural language. Ask it to remember a decision and it calls save_context. Ask it what you decided about authentication last week and it calls get_context with the right query.

The power of a shared vault is cross-client retrieval. Save an architectural decision during a Claude Code session, then pull it up in ChatGPT when explaining the system to a colleague. The entry exists once, accessible everywhere.

Tags and kinds stay consistent across all clients. Whether you save via MCP in Claude Code or via GPT Actions in ChatGPT, the same taxonomy applies. This consistency is what makes cross-client search reliable — the same query returns the same results regardless of which tool you use.

Latency stays under 200ms for hosted retrieval. GPT Actions add a small overhead for the HTTP round-trip, but the actual search and response time from Context Vault is fast enough that the Custom GPT experience feels native.

GPT Actions have rate limits and payload size constraints that vary by plan. Plus, Team, and Enterprise accounts get higher limits. Free-tier ChatGPT does not support Custom GPTs with actions, so this workflow requires a paid OpenAI plan.

Start with read-only access before enabling writes. Configure your Custom GPT with only the get_context action first. Once you are confident in the retrieval workflow, add save_context. This reduces the risk of accidentally saving low-quality entries from casual ChatGPT conversations.

Large vault responses may hit payload limits. If your search returns many results, use the limit parameter in get_context to cap the response size. Five to ten results is usually enough context for ChatGPT to work with effectively.