Low Risk

youtube_video_query

Ask Gemini about a YouTube video. Pass a video URL and any prompt — verbatim transcript with timestamps, summary, targeted Q&A about content or visuals, translation, etc. Works on any public/unlisted video.

Risk signalsAccepts URL/endpoint input (url)

Part of the Dialogbrain server.

youtube_video_query is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call youtube_video_query to retrieve information from Dialogbrain without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though youtube_video_query only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "youtube_video_query": {}
  }
}

See the full Dialogbrain policy for all 157 tools.

Get this rule live on your own Dialogbrain server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access youtube_video_query gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so youtube_video_query only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the youtube_video_query tool do? +

Ask Gemini about a YouTube video. Pass a video URL and any prompt — verbatim transcript with timestamps, summary, targeted Q&A about content or visuals, translation, etc. Works on any public/unlisted video.. It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on youtube_video_query? +

Register the Dialogbrain MCP server in PolicyLayer and add a rule for youtube_video_query: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Dialogbrain. Nothing to install.

What risk level is youtube_video_query? +

youtube_video_query is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit youtube_video_query? +

Yes. Add a rate_limit block to the youtube_video_query rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block youtube_video_query completely? +

Set action: deny in the PolicyLayer policy for youtube_video_query. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides youtube_video_query? +

youtube_video_query is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Dialogbrain tool call.

Deterministic rules across all 157 Dialogbrain tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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