Your files, put to work
Connect documents to your AI through MCP
MCP is a connection between an AI application and tools or data.
A document MCP server can expose tools to list files, search prepared text and read excerpts. You connect it through the AI app’s supported connector settings; pasting a URL into an ordinary chat does not install a connection.
Updated 17 October 2026 · OpenDataRoom · Fictional worked example
Your workflow at a glance
- Who it’s for
- Staff, customers, investors and advisers connecting their own AI
- The task
- The recipient can consult the selected collection without repeatedly attaching the same documents to chats
- Bring these materials
- Public sample contents.mdSample access scope.md
A fictional situation Fictional example: The sample contains public descriptions of OpenDataRoom.
Before you share
- Confirm your AI app supports this transport and authorisation
- Check readable text in your pack
- Copy the endpoint into connector settings
- Authorise only the intended scope
- Review the server-visible request record
How OpenDataRoom helps
The difference in your workflow
The recipient can consult the selected collection without repeatedly attaching the same documents to chats.
Keep the source documents organised in a drop, like a folder. Create named packs for different audiences, approve their access and let each recipient use their own AI. OpenDataRoom manages access and request records; the recipient’s AI performs the analysis.
- Your choice of compatible AI ↗
Use the connection settings in an app that supports the required MCP and authorisation flow. The public sample lets you try service information first.
- Source access for your AI ↗
Let a compatible AI list, search and read the supplied text. Ask it to cite the file and passage so you can check the answer.
- Named recipients and separate packs ↗
Choose who can connect. Prepare separate packs when people are allowed to see different documents.
A fictional example, shown one step at a time. No files are uploaded and no email or AI call is made.
01Choose the useful files
Start with the evidence for this question.
Prepare the approved versions of Public sample contents.md, Sample access scope.md. Check the readable text and leave unrelated material out.
Selected for this example · 2 documents
02Choose who gets access
Your pack. Your permission.
Use it with your own AI or invite a named recipient. Set a useful deadline. If people may see different documents, prepare separate packs.
- Recipient
- alex@example.com
- Example access window
- 24 hours
- Scope
- Only this selected pack
Verify email · approve access
Illustrative settings. No invitation is sent.
03Connect the recipient’s AI
Connect once. Choose approved packs.
Add OpenDataRoom in a compatible AI app’s connector settings and complete authorisation. A reusable connection lists only the packs you explicitly approve. Sending a URL into ordinary chat does not install the connection.
Example connection address
https://example.invalid/mcp/your-pack- Open connector settings.
- Add the real address from your pack.
- Complete authorisation.
The address shown here is deliberately inactive.
04Ask the question
Let the source material do useful work.
The recipient decides what to ask. Their AI can list, search and read the approved text; OpenDataRoom supplies access, not the AI model. The current tools return extracted text, not complete original files. Check the preparation report for material they cannot represent.
What can the public sample demonstrate?
Your ready-to-use prompt
05Check the answer
An answer you can trace back.
Ask for the source file and passage. Compare the result with the originals and keep assumptions or missing evidence visible.
A real service-information MCP connection and its disclosed request record, not access to customer files.
Open the source. Check the wording, date and conditions.
06See what was requested
A clearer handover and follow-up.
Review the requests received through the authorised connection. Investigate gaps, offer a clarification or end future access when the task is finished.
The record shows requests and prepared responses. It does not show the full chat or prove someone read an answer.
Look inside the example Fictional
These are invented teaching documents. The example demonstrates a workflow, not customer evidence or a verified outcome.
Read the 2 example sources
Public sample contents.md
The sample contains public descriptions of OpenDataRoom.
Sample access scope.md
It has no route to any subscriber's private pack.
More context for this workflow
Start with the receiving app
Check support for remote Streamable HTTP MCP and the required authorisation flow. OpenDataRoom’s server protocol has been tested, but individual commercial AI applications have not all been qualified. “Open to your choice of AI” means compatible applications, not universal support.
Prepare the files once for that pack
The browser prepares readable text while retaining originals in the package. Check the extracted text: standalone images and scans without embedded text are rejected. No OCR or chart interpretation is provided, and spreadsheet formulas are not recalculated. Search currently matches literal words in prepared text.
Let the AI choose its requests
The app may list files, search a term, then request a specific excerpt. It may retry with shorter terms. That request sequence is visible to the MCP server; the complete chat and the model’s reasoning are not.
Try the real public sample
Use the sample button on this page to get a temporary connection containing public OpenDataRoom information. No customer files are included. You can copy the URL or try direct tool requests in the browser. Those browser requests do not run an AI model.
Know what is recorded
Each sample launch has its own reference. Requests and public-document metadata are retained for 30 days for the operator. Anonymous launches identify a sample connection, not a verified person. Do not submit confidential search terms.
The useful detail
Questions about this workflow
What to prepare, how access works and what to expect from the result.
What would this change in my workflow?
The recipient can consult the selected collection without repeatedly attaching the same documents to chats. The practical task is to the recipient can consult the selected collection without repeatedly attaching the same documents to chats.
Which documents should I include?
For the example on this page, start with Public sample contents.md, Sample access scope.md. Use the approved versions relevant to the task and include source dates, definitions and any known gaps. Review the prepared text and disclosure scope before sharing. The fictional examples illustrate the structure; OpenDataRoom does not supply or verify your underlying research.
How do I keep the information current for staff or customers?
Assign a document owner, include version and effective dates, and review the content before sharing it. When a source file changes, use the drop update flow and review the affected permissions. After an update is accepted, fresh requests to existing packs serve the current versions of their selected files. Previously retrieved material in an AI chat does not refresh automatically. Newly added files stay private until selected, and changed excerpt selections need review. Access-change notices are queued for affected recipients; provider acceptance is not proof of inbox delivery. Tell recipients which effective date applies.
How does the recipient connect, and who gets access?
Choose your own AI or invite the intended email recipient. For a private invitation, the recipient verifies the authorised email and completes the required approval flow, then adds the connection in a compatible AI app’s settings. Forwarding an invitation alone does not authorise a different email. Permission covers the selected pack; create separate packs when audiences may see different documents.
Can I keep the files and end someone’s access?
Your wallet contains source drops, like folders. Each named pack shares selected files or passages from one drop. The source drop and its access packs have separate deadlines. You can keep the source until its displayed retention deadline while ending a recipient’s future access. Choose a sharing window, revoke it earlier when needed, and approve an extension deliberately. Expiry cannot recall material that an AI app or recipient has already retrieved.
What happens when I update or remove a file?
After you review and accept a drop update, fresh authorised requests receive the current versions of files already selected for their packs. This does not force an AI to fetch the files again or refresh its existing chat context. New files do not join those packs automatically. Review changed selected passages explicitly. Removing a file or its permission stops future retrieval, and access-change notices are queued for affected recipients. Provider acceptance is not proof of inbox delivery. This cannot erase content already retrieved.
What if my files or AI app are not supported?
Check the compatibility guide and try the public service-information sample first. An app must support the required MCP connection and authorisation; pasting a URL into ordinary chat does not install it. Use supported text-based files and inspect the preparation report. Originals are retained in encrypted storage, but the current AI tools return extracted text, not complete original files. Charts, images and layout are not faithfully delivered by those tools. Image-only scans and standalone images are refused; no OCR, audio transcription or spreadsheet recalculation is provided. Current pilot limits are listed on the pricing page.
What information is recorded about use of the pack?
The ledger records requests received by the service, authorised connection references, requested source information and preparation outcomes. Recipients can also deliberately submit a request, comment or note after approving the exact text. The owner sees that message, the verified connection identity and time in Recipient inbox for up to 90 days, unless deleted sooner. Received does not mean read or answered; no agent or automatic research is started. It does not record the entire AI conversation. Search text is supplied by the AI and may differ from the human question. An AI can reuse retrieved material without making another request. Literal search can miss relevant material; neither a search nor a file request proves that the AI reviewed the entire pack. Owners can investigate no-match searches before deciding whether to clarify wording, repair extraction or add information. A no-match result alone is not proof that a document is missing.
Who can read my files in the hosted connection?
Private-library file names and contents are encrypted in the browser with your wallet key. A hosted AI connection uses a separate access copy that you approve and that the service can decrypt for authorised retrieval. The receiving AI gets the content it retrieves and applies its own data policies. An optional desktop connector can decrypt locally, but it requires installation and is not a universal mobile connection. Review the security and compatibility pages before choosing a route.
Now it’s your turn
Try the whole journey.
Choose the example files and move through the connection, question, answer and ledger. The tour advances every four seconds; tap a step or Pause to take over.
No real upload, email or AI call is made. The connection in this fictional demo is inactive.
The six static steps and example sources above remain available if the interactive tour cannot load.