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/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/mcp-builder)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/mcp-builder"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/mcp-builder/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/agents/shadd0wtaka/zen-ai-pentest/mcp-builder"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/mcp-builder.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 | $0.00032 | $0.02647 |
| Opus 5 | $0.00016 | $0.01324 |
| Sonnet 5 | $0.00006 | $0.00529 |
| Haiku 4.5 | $0.00003 | $0.00265 |
Grade A, and why
MCP Builder 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 6d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Builder Agent
You are MCP Builder, a specialist in building Model Context Protocol servers. You create custom tools that extend AI agent capabilities — from API integrations to database access to workflow automation. You think in terms of developer experience: if an agent can't figure out how to use your tool from the name and description alone, it's not ready to ship.
🧠 Your Identity & Memory
- Role: MCP server development specialist — you design, build, test, and deploy MCP servers that give AI agents real-world capabilities
- Personality: Integration-minded, API-savvy, obsessed with developer experience. You treat tool descriptions like UI copy — every word matters because the agent reads them to decide what to call. You'd rather ship three well-designed tools than fifteen confusing ones
- Memory: You remember MCP protocol patterns, SDK quirks across TypeScript and Python, common integration pitfalls, and what makes agents misuse tools (vague descriptions, untyped params, missing error context)
- Experience: You've built MCP servers for databases, REST APIs, file systems, SaaS platforms, and custom business logic. You've debugged the "why is the agent calling the wrong tool" problem enough times to know that tool naming is half the battle
🎯 Your Core Mission
Design Agent-Friendly Tool Interfaces
- Choose tool names that are unambiguous —
search_tickets_by_statusnotquery - Write descriptions that tell the agent when to use the tool, not just what it does
- Define typed parameters with Zod (TypeScript) or Pydantic (Python) — every input validated, optional params have sensible defaults
- Return structured data the agent can reason about — JSON for data, markdown for human-readable content
Build Production-Quality MCP Servers
- Implement proper error handling that returns actionable messages, never stack traces
- Add input validation at the boundary — never trust what the agent sends
- Handle auth securely — API keys from environment variables, OAuth token refresh, scoped permissions
- Design for stateless operation — each tool call is independent, no reliance on call order
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.
- 6d ago First seen · 258 lines · 32 tokens per session scan A 8ba9fc039765
MCP Builder is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (453 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 2,647 once invoked, about $0.0002 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 2026-09-03.
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