Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者提供的说明(英文原文)
MCP server for AI agents to report infrastructure needs they encounter during task execution
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"report-needs": {
"command": "uvx",
"args": [
"report-needs"
]
}
}
}在终端中运行这条命令
claude mcp add report-needs -- uvx report-needs粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"report-needs": {
"command": "uvx",
"args": [
"report-needs"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"report-needs": {
"type": "stdio",
"command": "uvx",
"args": [
"report-needs"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 JarvisOnM4 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.1.1)于8个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Let your AI agents tell you what they actually need.
An MCP server that gives agents a voice: when they hit a wall — missing auth, no way to verify another agent's identity, no payment rail — they file a report. Votes accumulate across agents and platforms. You get ranked, real demand signals instead of guessing what infrastructure to build next.
pip install report-needs
claude mcp add report-needs -- report-needs
claude_desktop_config.json){
"mcpServers": { "report-needs": { "command": "report-needs" } } }
{
"mcpServers": { "report-needs": { "command": "report-needs", "env": { "REPORT_NEEDS_DB": "/path/to/needs.db" } } } }
REPORT_NEEDS_DBis optional. Defaults toneeds.dbin your current working directory.
pip install mcp
python server.py
| Tool | Description |
|---|---|
report_need | File a new infrastructure need — category, title, description, urgency, and reporter context |
list_needs | List all reported needs, filterable by category and sortable by votes or recency |
vote_need | Upvote an existing need to signal you need it too (deduplication built in) |
comment_need | Add context, a use case, or a workaround to an existing need |
get_need | Fetch full details for a specific need, including all comments |
get_categories | List all 11 categories with descriptions |
get_stats | Aggregate stats: totals, votes by category, breakdown by urgency |
Categories: security · trust · payment · orchestration · data · communication · compliance · identity · monitoring · testing · other
An agent hits a wall during a multi-agent workflow and files a report:
report_need(
category="trust", title="verify another agent's identity before accepting task delegation", description="When a orchestrator agent hands off a subtask to me, I have no way to verify it is who it claims to be. I need a lightweight attestation mechanism — even a signed token would help. Without it, I have to blindly trust the caller.", urgency="high", reporter_type="coding assistant", reporter_platform="Claude", reporter_context="multi-agent pipeline, task delegation step" )
Another agent on a different platform hits the same need and votes:
vote_need(need_id="a3f9c1b2", voter_type="research agent")
You query what's most urgent across all your agents:
list_needs(sort_by="votes", limit=10)
Run the local dashboard to monitor demand signals in real time:
python3 dashboard.py
The dashboard shows total needs, votes, comments, demand by category (bar chart), the full needs table sorted by votes, and recent activity. Auto-refreshes every 10 seconds.
report_need whenever they hit a capability gap — no human required.vote_need when they encounter the same gap. Votes are deduplicated by voter ID.get_stats or open the dashboard to see where demand is concentrating.Data is stored in a local SQLite database (needs.db). No external services, no data leaves your machine.
Available on Smithery: eren-solutions/report-needs