To reuse the same research across ChatGPT, Claude, and Codex, keep the source collection in a project-owned memory layer and connect compatible clients to it. Each tool should retrieve the passages needed for its current task instead of receiving another copied version of the entire brief.
The project becomes the stable context. The AI client becomes a replaceable working surface.
Separate the four layers
A portable research workflow has four distinct layers:
- Sources: Articles, videos, posts, interviews, documents, and exact passages.
- Project state: Current objective, constraints, decisions, and open questions.
- Retrieval: Search that selects relevant sources for the task.
- Instructions: The immediate request given to the AI tool.
Problems appear when these layers are compressed into one giant prompt. Sources lose provenance, project state becomes stale, and every client receives a slightly different copy.
Keep durable knowledge outside the conversation. Let the active prompt describe what this tool should do now.
Build one project memory
Choose a clear project, such as a launch, research report, or content series. Capture the relevant web material with its original URL and notes. Add important documents and a concise current-state summary.
For each source, preserve enough content to support retrieval. A bookmark title is not sufficient if the useful claim appears deep in the page.
Use project boundaries to avoid accidental mixing. Product research, customer discovery, and personal reading should not become one undifferentiated memory store.
Connect compatible clients
MCP provides a standard protocol through which AI applications can use resources and tools from external servers. The official architecture allows servers to run locally or remotely while the host controls connections and permissions.
SauceTab offers a hosted connection described in the MCP setup guide. A user authorizes a workspace and can use supported clients to search and read project research.
Client availability and setup can change. Follow the current instructions for ChatGPT, Claude, Codex, or any other client rather than relying on an old configuration snippet.
Give each tool a role
The same research does not mean every tool should do the same job.
- Use a research interface to collect and compare sources.
- Use Claude or ChatGPT for analysis, outlining, or drafting when appropriate.
- Use Codex or another coding agent to implement decisions in the repository.
- Use the project memory to keep all of them grounded in the same evidence.
For example, a writing assistant can produce a source-backed product brief. A coding agent can then search the same project for requirements and constraints before implementation.
Use a standard retrieval instruction
Put a short rule in each client's project instructions:
Search the connected SauceTab project before answering questions about this work. Cite the saved sources used, distinguish evidence from recommendations, and state when the project lacks the required information.
This instruction improves continuity without duplicating the project contents.
Keep permissions narrow
Most research tasks need search and read access. Do not grant write or deletion tools only because they are available.
Review:
- Which workspace the client can access.
- Whether the connection is read-only.
- Which tools are exposed.
- How access is revoked.
- Whether the client is appropriate for the data sensitivity.
Test with a known source before trusting the workflow. Ask the client to find the source, open the exact passage, and explain what it supports.
Update once, reuse everywhere
When the project changes, update the source collection or current-state note in one place. New retrievals can then use the latest context without replacing a prompt in every tool.
This does not make all clients identical. Models and tools will interpret the same evidence differently. The benefit is that their differences become easier to evaluate because they start from a shared, inspectable source base.
Portable research gives you model choice without repeatedly rebuilding project memory.