Remove outdated README content and gemini-extension.json

README.md

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-# Cloud Storage Managed MCP Extension
+# Google Cloud Storage Extension
 
-The Cloud Storage managed MCP extension allows users to manage buckets and
-objects via natural language commands.
-
-## Why use the Cloud Storage managed MCP server?
-
-Google and Google Cloud
-[managed MCP servers](https://docs.cloud.google.com/mcp/overview) can be used in
-your AI applications with enterprise-ready governance, security, and access
-control.
-
-## Before you begin
-
-1.  In the Google Cloud console, on the
-    [project selector page](https://console.cloud.google.com/projectselector2/home/dashboard),
-    select or create a Google Cloud project. > **Note**: If you don't plan to
-    keep the resources that you create in this > procedure, create a project
-    instead of selecting an existing project. > After you finish these steps,
-    you can delete the project, removing all > resources associated with the
-    project.
-2.  Get your administrator to grant you the
-    [MCP Tool User role](https://docs.cloud.google.com/iam/docs/roles-permissions/mcp#mcp.toolUser)
-    (`roles/mcp.toolUser`) on the Google Cloud project. If you created a new
-    project, then you already have the required permissions.
-3.  Ensure your administrator has enabled the
-    [Cloud Storage API](https://console.cloud.google.com/marketplace/product/google/storage.googleapis.com)
-    on the Google Cloud project.
-
-## Configure authentication
-
-This extension uses Google Application Default Credentials (ADC) to perform
-authentication. To login with ADC, run the following command in your terminal:
-`bash gcloud auth application-default login`
-
-For additional details, see the
-[ADC documentation](https://docs.cloud.google.com/docs/authentication/application-default-credentials#personal).
-
-## Available tools
-
-To see a complete list of available tools and their schemas, see the
-[Cloud Storage MCP reference](https://docs.cloud.google.com/storage/docs/reference/mcp).
-
-## Limitations
-
-The Cloud Storage MCP server has the following limitations:
-
--   **File types**: Read operations for content analysis are restricted to text,
-    PDF, and image files; write operations are restricted to text files.
--   **File size**: Maximum of 8 MiB for read and write operations.
--   **Endpoint**: Global endpoint only.
-
-For detailed information on quotas and limits that apply to the Cloud Storage
-MCP server, see
-[Quotas and limits](https://docs.cloud.google.com/storage/quotas#gcs-mcp).
-
-## Sample use cases
-
-The following are example use cases for the Cloud Storage MCP server.
-
-### Manage retail content and campaigns
-
-A sample use case for the Cloud Storage MCP server is to assist a retailer's
-marketing agent to create and manage product listings and promotional campaigns.
-The Cloud Storage MCP server lets you list, read, and write objects, and create
-buckets for storing product and campaign assets by using natural language.
-
-**Sample prompt**:
-
-"Create a product listing for SKU-123 using assets from the product-images
-bucket, then create a new bucket called campaign-q3-assets and generate and save
-banner images into it."
-
-**Workflow**: The workflow for creating product listings and campaigns might
-look like the following:
-
--   **List assets**: The agent uses list_objects to find all images for the new
-    product in a dedicated Cloud Storage bucket.
--   **Fetch content**: The agent uses read_object to access product assets (up
-    to 8 MiB in size), and also fetches product descriptions from a product
-    information management (PIM) system using another tool. Generate listing:
-    The agent generates a draft of the product listing, including marketing copy
-    and links to the images and videos.
--   **Create campaign bucket**: The agent uses create_bucket to create a new
-    bucket for campaign assets. Save campaign assets: The agent generates
-    campaign assets (for example, banners) and uses write_text to save them into
-    the new "campaigns" bucket. Each asset must be less than 8 MiB in size.
-
-### Analyze financial data
-
-A sample use case for the Cloud Storage MCP server is to help portfolio managers
-gain insights from financial reports and audio recordings of trader calls with
-clients. The Cloud Storage MCP server helps the agent to identify and download
-relevant documents and pass them to an LLM for analysis.
-
-**Sample prompt**:
-
-"What were the key takeaways from ExampleCorp's most recent earnings call, and
-how does that compare to the sentiment in their last three financial reports?"
-
-**Workflow**: The workflow for analyzing financial documents might look like the
-following:
-
--   **Identify documents**: The agent extracts keywords from the user's question
-    to identify relevant buckets or prefixes, for example,
-    earnings-calls/ExampleCorp/ or financial-reports/ExampleCorp/ and uses
-    list_objects to find relevant audio transcripts and financial reports.
--   **Download content**: The agent uses read_text or read_object to download
-    the content of the identified files, up to 8 MiB per file.
--   **Analyze and respond**: The agent passes the content to an LLM to summarize
-    findings, compare sentiment, and synthesize an answer to the user's
-    question. If needed, other tools like BigQuery can be used for deeper
-    analysis.
-
-### Assess vendor risk
-
-A sample use case for the Cloud Storage MCP server is to help automate the
-initial vendor risk assessment process for a bank's risk management team. The
-Cloud Storage MCP server lets the AI agent fetch and analyze documents that
-vendors submit to identify potential risks by using natural language.
-
-**Sample prompt**:
-
-"Assess vendor 'Example Inc.' by reviewing their latest security questionnaire
-and compliance certificate in the vendor-docs bucket. Summarize any potential
-risks based on our policies and save the report."
-
-**Workflow**: The workflow for assessing vendor risk might look like the
-following:
-
--   **Find documents**: The agent uses the list_objects tool to find the
-    vendor's folder in a Cloud Storage bucket dedicated to vendor documents.
--   **Download documents**: The agent uses read_object to download all relevant
-    documents, such as security questionnaires, compliance certificates, and
-    financial statements, up to 8 MiB per file.
--   **Analyze documents**: The agent analyzes the content of these documents,
-    possibly using other tools to extract text, to look for red flags or missing
-    information based on the bank's risk policies.
--   **Compile and save report**: The agent compiles a summary report of its
-    findings and uses write_text to save it to the vendor's folder in Cloud
-    Storage for the risk assessor to review.
-
-## Optional security and safety configurations
-
-MCP introduces new security risks and considerations due to the wide variety of
-actions that you can take with MCP tools. To minimize and manage these risks,
-Google Cloud offers defaults and customizable policies to control the use of MCP
-tools in your Google Cloud organization or project. For more information about
-MCP security and governance, see
-[AI security and safety](https://docs.cloud.google.com/mcp/ai-security-safety).
-
-## Quotas and limits
-
-The Cloud Storage MCP server doesn't have its own quotas. There is no limit on
-the number of calls that can be made to the MCP server. You are still subject to
-the quotas enforced by the APIs called by the MCP server tools.
-
-## Reference and resources
-
-*   Explore the
-    [Cloud Storage remote MCP server reference documentation](https://docs.cloud.google.com/storage/docs/reference/mcp),
-    which includes a list of all available tools, and the full input and output
-    schema for each tool.
-*   See the
-    [Cloud Storage overview](https://docs.cloud.google.com/storage/docs/introduction).
-*   Learn about
-    [MCP security and governance](https://docs.cloud.google.com/mcp/ai-security-safety).
+> Documentation is being updated. Content coming soon.