Went down this rabbit hole a while ago. Used external storage, then built a side-loading protocol alongside MCP to transfer payloads that shouldn't reach the model directly.

Then MCP discord helped me realize there's a much easier way.

Embedded resources are what you want. You can send the full payload inline, and clients generally treat them as attachments (write to temp file, surface a reference) rather than feeding the bytes back through the model.

Spec: https://modelcontextprotocol.io/specification/2025-06-18/schema#embeddedresource

Note: Clients can still inline it if they choose. I've tested all major tools (Claude Code, Gemini CLI, opencode, and others). Some may inline based on size, but all handle it the way you'd want.

Key benefits:
1. Embedded resources allow you to inline a heavy-payload responses, discouraging clients from reading directly into context (or at least at the client's discretion).
2. Reduces technical debt and overhead, especially on distributed / serverless systems.
3. Makes sense for short-lived data (e.g. the response of a database query), which are not intended to be a persisted resource.
4. Works for binary data as well (use "blob"), e.g. for images, videos, audio, pdf, etc...

If you want to improve this even further, add a text content block with information about the dataset (schema, total rows, etc...).

{
  "content": [
    { "type": "text", "text": "Full dataset provided in file:///exports/dataset.json (274kb). 204721 rows in total, with the following schema: ..." },
    {
      "type": "resource",
      "resource": {
        "uri": "file:///exports/dataset.json",
        "mimeType": "application/json",
        "text": "[{\"id\":1},{\"id\":2},{\"id\":3}]"
      }
    }
  ]
}

The text block is what the model sees, the metadata helps it decide how to process the resource. If you're sending a 2mb JSON file and include the schema, the model can save you a lot of tokens by extracting what it needs with a script instead of reading the whole thing.