panel-review MCP server
Enables adversarial multi-model AI review via OpenRouter, allowing Claude to submit prompts to multiple models and compare responses through custom connector tools.
README
panel-review MCP server — deployment
Adversarial multi-model review via OpenRouter, callable from Claude as a custom connector.
Tested before packaging: server starts, serves /mcp over streamable-http, all five tools
register, and the health check reports cleanly when the API key is absent rather than crashing.
Files
| File | Purpose |
|---|---|
panel_mcp_server.py |
The server. |
requirements.txt |
mcp and httpx. Railway installs these automatically. |
railway.json |
Tells Railway how to build and start. Means you don't set the start command by hand. |
Procfile |
Fallback for hosts that read a Procfile instead. Harmless on Railway. |
Deploy
- Put all four files in a GitHub repo, at the top level (not in a subfolder).
- Railway → New Project → Deploy from GitHub repo → pick the repo.
- Railway service → Variables → New Variable:
OPENROUTER_API_KEY= your key. Do this before worrying about the first deploy failing. - Railway service → Settings → Networking → Generate Domain. Without this the service runs but has no public URL. Port 8000 if asked.
- Your connector URL is
https://<the-domain-railway-gave-you>/mcp— note the/mcp.
Connect to Claude
Customize > Connectors > + > Add custom connector, paste the URL, Add. Then enable it per conversation via the + button > Connectors.
First thing to run
Ask Claude to run panel_health. It confirms the server is reachable and the key is set.
Then panel_models with a filter (deepseek, gemini, gpt) to get slugs that currently
resolve — the defaults in the file will drift and should not be trusted.
Optional environment variables
| Variable | Default | Purpose |
|---|---|---|
PANEL_MAX_DOC_CHARS |
400000 | Rejects oversized documents. |
PANEL_MAX_MODELS |
6 | Caps models per call. |
PANEL_MAX_CALLS_PER_DAY |
200 | Daily spend guard. |
Two things to know
Spend exposure. This endpoint spends your OpenRouter key and the URL is the only thing protecting it. Anyone who learns the URL can burn credits. The caps above limit the damage but do not prevent it. Set a spend limit in the OpenRouter dashboard as well, and treat the URL as a secret. If it leaks, delete the Railway domain and generate a new one.
Job state is in memory. panel_submit and panel_status share state within one process.
A redeploy loses running jobs, and if you ever scale to more than one replica the poll can land
on the wrong instance and report "unknown". Stay on a single replica.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。