Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者提供的说明(英文原文)
A semantic search engine for files and code.
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。
VEXOR_API_KEY, VEXOR_REMOTE_RERANK_API_KEY, VEXOR_CONFIG_JSON标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
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": {
"vexor": {
"command": "uvx",
"args": [
"vexor",
"mcp"
],
"env": {
"VEXOR_API_KEY": "your-value",
"VEXOR_REMOTE_RERANK_API_KEY": "your-value",
"VEXOR_CONFIG_JSON": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add vexor --env VEXOR_API_KEY=your-value --env VEXOR_REMOTE_RERANK_API_KEY=your-value --env VEXOR_CONFIG_JSON=your-value -- uvx vexor mcp运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"vexor": {
"command": "uvx",
"args": [
"vexor",
"mcp"
],
"env": {
"VEXOR_API_KEY": "your-value",
"VEXOR_REMOTE_RERANK_API_KEY": "your-value",
"VEXOR_CONFIG_JSON": "your-value"
}
}
}
}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": {
"vexor": {
"type": "stdio",
"command": "uvx",
"args": [
"vexor",
"mcp"
],
"env": {
"VEXOR_API_KEY": "your-value",
"VEXOR_REMOTE_RERANK_API_KEY": "your-value",
"VEXOR_CONFIG_JSON": "your-value"
}
}
}
}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。
VEXOR_CONFIG_JSON env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8日
注册名称已通过 GitHub 账户 scarletkc 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。
你需要填写:VEXOR_API_KEY, VEXOR_REMOTE_RERANK_API_KEY
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(0.31.0)于10天前进入注册中心。
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。
Vexor is a semantic search engine that builds reusable indexes over files and code. It supports configurable embedding and reranking providers, and exposes the same core through a Python API, a CLI tool, and an MCP server.
Vexor Demo Video
Vexor has been recognized and featured by the community:
When you remember what a file *does* but forget its name or location, Vexor finds it instantly—no grep patterns or directory traversal needed.
Designed for both humans and AI coding assistants, enabling semantic file discovery in autonomous agent workflows.
Download standalone binary from releases (no Python required), or:
pip install vexor # also works with pipx, uv
vexor init
The wizard also runs automatically before the first interactive operational command when no config exists. Configuration-management (vexor config), MCP, help, and version commands run directly.
vexor "api client config" # defaults to search current directory
vexor search "api client config" --path ~/projects/demo --top 5
vexor search "api client config" --no-cache
Vexor auto-indexes on first search. Example output:
Vexor semantic file search results
──────────────────────────────────
1 0.923 ./src/config_loader.py - config loader entrypoint 2 0.871 ./src/utils/config_parse.py - parse config helpers 3 0.809 ./tests/test_config_loader.py - tests for config loader
vexor index # indexes current directory
vexor index --path ~/projects/demo --mode code
Useful for CI warmup or when `auto_index` is disabled.
Vexor can also be imported and used directly from Python:
from vexor import index, search
index(path=".", mode="head") response = search("config loader", path=".", mode="name")
for hit in response.results: print(hit.path, hit.score)
Configuration follows the same global and project-level resolution as the CLI. For runtime overrides, cache controls, and per-call options, see docs/api/python.md.
This repo includes a skill for AI agents to use Vexor effectively:
vexor install --skills claude # Claude Code
vexor install --skills codex # Codex
Skill source: plugins/vexor/skills/vexor-cli
[!NOTE]
The Agent Skill and the MCP server provide the same core capability — pick one per agent.
The skill teaches shell-capable agents (Claude Code, Codex) to drive the full CLI and assumes
vexoris installed on PATH; the MCP server exposes search as native tools, works in any MCP client (Cursor, Windsurf, Zed, ...), and can bootstrap without prior setup viauvxand environment variables.
Vexor ships a built-in MCP stdio server, so any MCP-capable agent can use semantic file search as a native tool:
claude mcp add vexor -- vexor mcp # Claude Code
codex mcp add vexor -- vexor mcp # Codex
Or configure manually in any MCP client, optionally supplying the API key and any config overrides via env (no vexor init needed):
{
"mcpServers": { "vexor": { "command": "vexor", "args": ["mcp"], "env": { "VEXOR_API_KEY": "sk-...", "VEXOR_CONFIG_JSON": "{\"provider\": \"gemini\", \"rerank\": \"bm25\"}" } } } }
The server exposes two tools: vexor_search (semantic file search, returning the matching source text so an agent rarely needs a follow-up file read) and vexor_index (explicit index warm-up). No extra dependencies are required. Vexor is listed on the official MCP registry as io.github.scarletkc/vexor. See docs/mcp.md for tool schemas, environment variables, and client setup details.
vexor init # guided setup (recommended)
vexor config --set-api-key "YOUR_KEY" # or env: VEXOR_API_KEY / OPENAI_API_KEY / ... vexor config --set-provider openai # default; also gemini/voyageai/custom/local vexor config --rerank hybrid # optional: fuse exact keyword + semantic ranking vexor config --show # view effective settings and origins
Global config lives in ~/.vexor/config.json; the nearest /.vexor/config.json can override a restricted set of behavior fields for that project. Non-secret fields can also be injected via VEXOR_CONFIG_JSON (useful for MCP clients and CI), and fully offline use is supported through local embedding models.
See docs/configuration.md for the complete reference: project config fields and precedence, all config commands, API keys and environment variables, rerank strategies (hybrid / BM25 / FlashRank / remote), remote vs local providers, embedding dimensions, and offline local model setup.
Everyday usage fits in vexor "query", vexor search, and vexor index (see Quick Start). The full command table, common flags, index modes (--mode auto/name/head/brief/full/code/outline), .vexorignore files, project-local indexes (vexor index --local), cache behavior, and porcelain output format are documented in docs/cli.md.
Contributions, issues, and PRs welcome! Commit messages and PR titles follow Conventional Commits (e.g. feat(mcp): add stdio server). Star if you find it helpful.