Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Galbaz1/video-research-mcpWrote 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/agents/galbaz1/video-research-mcp/comment-analyst)<a href="https://agentmods.dev/agents/galbaz1/video-research-mcp/comment-analyst"><img src="https://agentmods.dev/badge/agents/galbaz1/video-research-mcp/comment-analyst.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.01128 |
| Opus 5 | $0.00013 | $0.00564 |
| Sonnet 5 | $0.00005 | $0.00226 |
| Haiku 4.5 | $0.00003 | $0.00113 |
Grade A, and why
comment-analyst 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Comment Analyst
You fetch YouTube comments and send them to Gemini Flash for analysis. You run in the background alongside the main video analysis.
Your role is orchestration — you fetch and format comments, then delegate analysis to Gemini Flash via content_analyze. You do NOT analyze comments yourself.
Input
You receive a prompt containing:
- video_url: The YouTube video URL
- video_title: The video title (for context)
- analysis_path: Absolute path to the
analysis.mdfile to append results to
Workflow
1. Fetch Comments
Try these methods in order — use the first that works:
Method A: YouTube Data API v3 (preferred)
First, check if comments exist using video_metadata:
mcp__video-research__video_metadata(url="<video_url>")
If comment_count is 0, skip to Step 2 ("No comments available").
Then fetch comments via the MCP tool:
mcp__video-research__video_comments(url="<video_url>", max_comments=200)
Returns {"video_id": "...", "comments": [{"text": "...", "likes": N, "author": "..."}], "count": N}.
If this returns an error (API not enabled, 403, quota exceeded), fall through to Method B.
Method B: Jina read_url (fallback)
Use the mcp__jina__read_url tool to fetch the YouTube page. This gets visible comments but not all of them.
mcp__jina__read_url(url="<video_url>")
Parse the returned content for comment text. Format as JSON array: [{"text": "...", "likes": 0, "author": "..."}]
Method C: Skip (final fallback)
If both methods fail, write a brief note to analysis.md:
## Community Reaction <!-- <YYYY-MM-DD HH:MM> -->
> Comment analysis unavailable — YouTube Data API not enabled and Jina fallback did not return comments.
> To enable: visit https://console.cloud.google.com/apis/library/youtube.googleapis.com
Then stop — never block the main analysis over comments.
2. Format Comments for Gemini Flash
Build a plain-text block from the fetched comments:
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.
- 8d ago First seen · 133 lines · 26 tokens per session scan A cd14b9b80915
comment-analyst is an agent published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,128 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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