AgentDesk MCP

Name: AgentDesk MCP
Author: Rih0z

@Rih0z

关于 AgentDesk MCP

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.

#ai #quality-assurance #code-review #developer-tools #mcp

基本信息

分类

开发工具

传输方式

stdio

发布者

Rih0z

提交者

岩男祐多

配置

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/[email protected]"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is AgentDesk MCP?

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.

How to use AgentDesk MCP?

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.

Key features of AgentDesk MCP

Adversarial prompting that assumes the author made mistakes
Evidence-based checklist with automatic downgrade for missing evidence
Anti‑gaming validation forces FAIL if >30% of items lack evidence
Dual adversarial review with two independent reviewers
One‑tool MCP setup, no SDK required
BYOK (bring your own API key) — uses your own Anthropic API key

Use cases of AgentDesk MCP

Code review: check for bugs, security issues, and performance problems
Content review: verify accuracy, readability, SEO, and audience fit
Factual verification: validate claims in AI-generated text
Translation quality: assess accuracy and naturalness
Data extraction: verify completeness and correctness of extracted data

FAQ from AgentDesk MCP

What makes adversarial review different from self‑review?

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.

How does dual review work?

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.

What are the limitations of AgentDesk MCP?

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.

What are the system requirements?

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.

How does AgentDesk MCP compare to other evaluation tools?

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.

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