fastmcp/docs/apps/providers/file-upload.mdx

---
title: File Upload
sidebarTitle: File Upload
description: Drag-and-drop file upload for any MCP server
icon: upload
tag: NEW
---

import { VersionBadge } from '/snippets/version-badge.mdx'

<VersionBadge version="3.2.0" />

`FileUpload` adds drag-and-drop file upload to any server. Users upload files through an interactive UI, bypassing the LLM context window entirely. The LLM can then list and read uploaded files through model-visible tools.

<Frame>
  <img src="/apps/images/app-file-upload.png" alt="The FileUpload provider shown in Goose, with a drag-and-drop zone for uploading files" />
</Frame>

```python
from fastmcp import FastMCP
from fastmcp.apps.file_upload import FileUpload

mcp = FastMCP("My Server")
mcp.add_provider(FileUpload())
```

This registers four tools:

| Tool | Visibility | Purpose |
|------|-----------|---------|
| `file_manager` | Model | Opens the drag-and-drop upload UI |
| `store_files` | App only | Called by the UI when the user clicks Upload |
| `list_files` | Model | Returns metadata for all uploaded files |
| `read_file` | Model | Returns a file's contents by name |

The LLM sees `file_manager`, `list_files`, and `read_file`. It calls `file_manager` to show the upload interface, then uses `list_files` and `read_file` to work with whatever the user uploaded. `store_files` is app-only — the UI calls it directly and the LLM never needs to know about it.

## Configuration

```python
FileUpload(
    name="Files",                    # App name (used in tool routing)
    max_file_size=10 * 1024 * 1024,  # 10 MB default, enforced server-side
    title="File Upload",             # Heading shown in the UI
    description="Drop files to...",  # Description text below the heading
    drop_label="Drop files here",    # Label inside the drop zone
)
```

The `max_file_size` limit is enforced both in the UI (the DropZone rejects oversized files) and on the server (the `store_files` tool validates before calling `on_store`).

## Storage Scoping

By default, files are stored in memory and scoped by MCP session ID. Each session gets its own isolated file store — files uploaded in one conversation aren't visible in another.

This works with **stdio**, **SSE**, and **stateful HTTP** transports, where sessions persist across requests.

<Warning>
In **stateless HTTP** mode, each request creates a new session object with a new ID. Files stored during one request (e.g. the UI upload) will be invisible to the next request (e.g. the LLM calling `list_files`). You **must** override `_get_scope_key` to use a stable identifier like a user ID from your auth token.
</Warning>

For stateless deployments, override `_get_scope_key` to return a stable identifier. For example, to scope files by authenticated user:

```python
from fastmcp.apps.file_upload import FileUpload

class UserScopedUpload(FileUpload):
    def _get_scope_key(self, ctx):
        return ctx.access_token["sub"]
```

For process-wide shared storage (all users see all files):

```python
class SharedUpload(FileUpload):
    def _get_scope_key(self, ctx):
        return "__shared__"
```

## Custom Storage

The default implementation stores files in memory for the lifetime of the server process. For persistent storage, subclass `FileUpload` and override three methods. Each receives the current `Context`, giving you access to session IDs, auth tokens, and request metadata for partitioning and authorization.

```python
import base64

from fastmcp.apps.file_upload import FileUpload

class S3Upload(FileUpload):
    def on_store(self, files, ctx):
        user_id = ctx.access_token["sub"]
        for f in files:
            s3.put_object(
                Bucket="uploads",
                Key=f"{user_id}/{f['name']}",
                Body=base64.b64decode(f["data"]),
            )
        return self.on_list(ctx)

    def on_list(self, ctx):
        user_id = ctx.access_token["sub"]
        objects = s3.list_objects(Bucket="uploads", Prefix=f"{user_id}/")
        return [
            {
                "name": obj["Key"].split("/", 1)[1],
                "type": "application/octet-stream",
                "size": obj["Size"],
                "size_display": f"{obj['Size']} B",
                "uploaded_at": obj["LastModified"].isoformat(),
            }
            for obj in objects.get("Contents", [])
        ]

    def on_read(self, name, ctx):
        user_id = ctx.access_token["sub"]
        obj = s3.get_object(Bucket="uploads", Key=f"{user_id}/{name}")
        content = obj["Body"].read()
        return {
            "name": name,
            "size": obj["ContentLength"],
            "type": obj["ContentType"],
            "uploaded_at": obj["LastModified"].isoformat(),
            "content": content.decode("utf-8"),
        }
```

Each file dict passed to `on_store` contains `name`, `size`, `type`, and `data` (base64-encoded content). The return value from `on_store` and `on_list` should be a list of summary dicts with `name`, `type`, `size`, `size_display`, and `uploaded_at` fields — these populate the file list in the UI.

`on_read` returns a dict with file metadata and either `content` (decoded text) or `content_base64` (a base64 preview for binary files).