Borrowing it
Nothing to install: this file belongs to hzy9981/vertex-ai-mcp-bridge. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hzy9981/vertex-ai-mcp-bridge/main/GEMINI.mdgit clone --depth 1 https://github.com/hzy9981/vertex-ai-mcp-bridgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/hzy9981/vertex-ai-mcp-bridge/gemini-md)<a href="https://agentmods.dev/instructions/hzy9981/vertex-ai-mcp-bridge/gemini-md"><img src="https://agentmods.dev/badge/instructions/hzy9981/vertex-ai-mcp-bridge/gemini-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/hzy9981/vertex-ai-mcp-bridge/gemini-md"><img src="https://agentmods.dev/badge/instructions/hzy9981/vertex-ai-mcp-bridge/gemini-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.05631 | $0.05631 |
| Opus 5 | $0.02815 | $0.02815 |
| Sonnet 5 | $0.01126 | $0.01126 |
| Haiku 4.5 | $0.00563 | $0.00563 |
Grade A, and why
vertex-ai-mcp-bridge GEMINI.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to vertex GEMINI.md — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vertex AI Gemini CLI Extension
This extension provides tools to manage prompts and use prompt optimization in Vertex AI directly from the Gemini CLI.
Available Tools
Prompt Management Tools
create_prompt: To save or create new prompts.read_prompt: To retrieve existing prompts by ID or display name.update_prompt: To modify existing prompts.delete_prompt: To remove prompts.list_prompts: To search and list prompts, useful for finding IDs.
Prompt Optimization Tools
run_few_shot_optimization: optimize user prompt based on examples provided in the CSV file stored in the GCS bucket.run_data_driven_optimize: Starts a data-driven prompt optimization job on Vertex AI using a configuration file stored in GCS.analyze_data_driven_optimize_results: Analyzes the output of a Data-Driven Optimize job to identify trends and best-performing candidates.generate_html_report: Generates a comprehensive HTML report with visualizations to help you understand optimization performance.write_data_driven_optimize_config: Constructs and uploads a new JSON configuration for optimization jobs, incorporating suggested tuning parameters.
External Tool Proxy
call_dashscope_mcp: Proxies calls to the Alibaba DashScope MCP service. RequiresDASHSCOPE_API_KEY.
Detailed Instructions for create_prompt Parameters
When using tools.create_prompt, pay special attention to how the following
arguments are sourced:
-
content(string, required):- Scenario 1: Saving the Last User Prompt: When the user issues a command
like "save last prompt", "save this prompt", or similar, indicating they
want to store their previous input:
- Examine the conversation history.
- Identify the most recent message with a
roleof "user". - Extract the
textcontent from this latest "user" message.
- Scenario 2: Creating from Explicitly Provided Content: If the user
provides the prompt content directly within the command (e.g., "Create a
prompt... with content '...'")
- Use the explicitly provided content.
- If no content can be determined from either scenario, pass an empty string.
- Scenario 1: Saving the Last User Prompt: When the user issues a command
like "save last prompt", "save this prompt", or similar, indicating they
want to store their previous input:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 453 lines · 5,631 tokens per session scan A a9488e32c351
vertex-ai-mcp-bridge GEMINI.md is an instructions file published in the GitHub repository hzy9981/vertex-ai-mcp-bridge (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 5,631 tokens to every session, about $0.0282 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to vertex GEMINI.md, differing in 3 lines, and is treated as a copy.
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