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/DollhouseMCP/mcp-serverWrote 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/dollhousemcp/mcp-server/test-generator)<a href="https://agentmods.dev/agents/dollhousemcp/mcp-server/test-generator"><img src="https://agentmods.dev/badge/agents/dollhousemcp/mcp-server/test-generator/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/dollhousemcp/mcp-server/test-generator"><img src="https://agentmods.dev/badge/agents/dollhousemcp/mcp-server/test-generator.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 | $0.00013 | $0.03970 |
| Opus 5.5 | $0.00005 | $0.01588 |
| Sonnet 5.5 | $0.00003 | $0.00794 |
| Haiku 4.5 | $0.00001 | $0.00397 |
Grade A, and why
Test Generator 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 1mo 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 1mo ago First seen · 616 lines · 13 tokens per session scan A d8610e29c61e
Test Generator is an agent published in the GitHub repository DollhouseMCP/mcp-server (46 stars, last pushed today), licensed AGPL-3.0. It adds 13 tokens to every session and 3,970 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
Code Reviewer
Automated code review agent with security, performance, and quality analysis.
tdd-coach
Test-Driven Development coach.
ecosystem-specialist
Multi-language build, test, and lint specialist. Detects which ecosystems a change set touches and runs the correct verification commands for each. Use proactively after code changes, or when the user says 'build', 'test', 'lint', or 'check'.
tdd-guide
Test-Driven Development specialist — enforces red-green-refactor with AAA, test doubles and behavior-first coverage. Use when implementer delegates test-first discipline or task explicitly requires TDD; opt-in via holistic caller — not a daily entry point.
tdd-guide
Test-Driven Development specialist. Write tests first, then implement minimal code to pass.
ndv-tester
Test generation specialist. Use when writing tests, improving coverage, or ensuring correctness. Adversarial by default — assumes the code is lying, treats every untested assumption as a hidden bug, cannot accept a happy path test as proof of anything.