@Rih0z
Adversarial AI review API — independent AI reviews another AI's output. Stop LLMs from grading their own homework. Provides automated quality assurance for AI-generated code, content, and other outputs through independent review pipelines.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"agentdesk-mcp": {
"command": "npx",
"args": [
"-y",
"@ezark-publish/agentdesk-mcp@1.3.0"
]
}
}
}Adversarial quality review of any AI-generated output. An independent reviewer assumes the author made mistakes and actively looks for problems. Returns structured verdict (PASS/FAIL/CONDITIONAL_PASS), score (0-100), categorized issues with severity, and evidence-based checklist. Works for any output type: code, content, summaries, translations, data extraction, etc.
Dual adversarial review: two independent reviewers assess the output from different angles, then a merge agent combines their findings. Stricter than single review — if either reviewer finds a critical issue, the merged verdict is FAIL. Use for high-stakes outputs where quality is critical.
List all available services on the AgentDesk marketplace. Returns service catalog with pricing, quality scores, and capabilities. Filter by category, minimum quality score, maximum price, or capability.
Execute a service on the AgentDesk marketplace. Requires an AgentDesk API key for authentication. Pass service-specific input parameters.
AgentDesk MCP is an MCP tool that provides independent adversarial quality review for any AI-generated output. It integrates with Claude Code, Claude Desktop, and any MCP client, and uses your own Anthropic API key (BYOK) to perform structured, bias-resistant evaluations.
Install via npx agentdesk-mcp and add it to your MCP client, setting the ANTHROPIC_API_KEY environment variable. Two tools are provided: review_output for a single adversarial review, and review_dual for a dual‑reviewer pass with a merged verdict. Pass the AI output to review, optionally customizing criteria and review type.
Self‑review has a systematic leniency bias because the same model shares blind spots that created errors. AgentDesk uses a separate reviewer invocation with adversarial prompting, which is fundamentally different and more rigorous.
Dual review (`review_dual`) runs two independent adversarial reviews from different angles, then a merge agent combines findings. If either reviewer finds a critical issue, the merged verdict is FAIL, and the lower score is taken. Issues are combined and deduplicated.
Like all LLM‑as‑judge systems, it can be vulnerable to prompt injection. The anti‑gaming validation layer mitigates superficial gaming, but determined adversarial inputs remain a challenge. Each `review_output` call makes 1 LLM API call; `review_dual` makes 3, so factor BYOK cost into your pipeline.
You need an `ANTHROPIC_API_KEY` environment variable. The default reviewer model is Claude Sonnet 4‑6, but you can specify another model. No other SDK or dependencies are required.
AgentDesk MCP offers one‑tool MCP setup, adversarial review, dual reviewer capability, and anti‑gaming validation — features not found in manual prompts, Braintrust, or DeepEval. It is MCP native and requires no SDK.
The tools your AI needs to make your games. Turn prompts into production-ready game assets — animated characters with their weapons attached, seamless textures, terrain, VFX, HUDs — and playable game kits Unity, Godot, U
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