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/iusztinpaul/designing-real-world-ai-agents-workshopWrote 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/mcp/iusztinpaul/designing-real-world-ai-agents-workshop/linkedin-writer)<a href="https://agentmods.dev/mcp/iusztinpaul/designing-real-world-ai-agents-workshop/linkedin-writer"><img src="https://agentmods.dev/badge/mcp/iusztinpaul/designing-real-world-ai-agents-workshop/linkedin-writer/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/mcp/iusztinpaul/designing-real-world-ai-agents-workshop/linkedin-writer"><img src="https://agentmods.dev/badge/mcp/iusztinpaul/designing-real-world-ai-agents-workshop/linkedin-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
linkedin-writer 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 12d 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
100% identical to linkedin-writer — 0 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.
What it actually says
{
"linkedin-writer": {
"command": "uv",
"args": [
"run",
"fastmcp",
"run",
"src/writing/server.py"
],
"env": {
"ENV_FILE_PATH": ".env"
}
}
}What else .mcp.json configures
This page is one entry in a file that holds 2. Installing the file brings all of them; each is measured and scanned on its own page.
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.
- 12d ago First seen · 14 lines scan A 5910be1f5c3f
linkedin-writer is an MCP server published in the GitHub repository iusztinpaul/designing-real-world-ai-agents-workshop (505 stars, last pushed 3mo ago), licensed MIT. Its token cost is not measured: an MCP server costs its tool schemas, not its config file. A static security scan graded it A with 0 findings. It is 100% identical to linkedin-writer, differing in 0 lines, and is treated as a copy.
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