Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者提供的说明(英文原文)
Token-efficient MCP server for codebase search, NuGet decompilation, and deep PDF reading.
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": {
"dotnetsearch": {
"command": "dnx",
"args": [
"PuneethDC.DotNetSearch",
"--yes"
]
}
}
}在终端中运行这条命令
claude mcp add dotnetsearch -- dnx PuneethDC.DotNetSearch --yes粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"dotnetsearch": {
"command": "dnx",
"args": [
"PuneethDC.DotNetSearch",
"--yes"
]
}
}
}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": {
"dotnetsearch": {
"type": "stdio",
"command": "dnx",
"args": [
"PuneethDC.DotNetSearch",
"--yes"
]
}
}
}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 puneethdc99 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(1.0.3)于11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 .NET。
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。
MCP Server Identifier:
mcp-name: io.github.puneethdc99/dotnetsearch
*(Proof of package ownership for GitHub MCP Registry inclusion)*
DotNetSearch is a self-contained MCP (Model Context Protocol) server that empowers GitHub Copilot, Claude, Cursor, and other AI agents with high-performance codebase search, NuGet package decompilation, whitespace token reduction, and deep PDF document reading and analysis:
| Category | Tool | Purpose |
|---|---|---|
| Code Search | search | Find files and lines matching a keyword or natural-language query — returns only matching lines (~110 tokens vs ~2500 for a full file read) |
| Code Search | search_related | Find code chunks structurally similar to a known location — ideal for discovering callers, implementations, or patterns |
| Decompilation | decompile_type | Decompile any .NET type from referenced NuGet package assemblies — returns the full C# class definition without needing the source code |
| Optimization | compress_code | Reduce C# code indentation from 4-space to 1-space per indent level and remove blank lines — saves 10–20% tokens with zero semantic change |
| File Reading | read_file | Read a file from disk and optionally return a specific line range — useful after search when you need full file context |
| PDF Reading | read_pdf_text | Extract clean, structured text page-by-page or across targeted page ranges from any PDF document |
| PDF Reading | get_pdf_info | Inspect PDF metadata, total page count, per-page dimensions, author, subject, creation date, and structure |
| PDF Visuals | render_pdf_page_as_image | Rasterize any PDF page into a high-resolution PNG image at custom DPI — ideal for inspecting charts, diagrams, tables, and scanned documents |
| PDF Visuals | extract_pdf_images | Extract embedded raster images from PDF pages and save to disk with pixel coordinates |
| PDF Structure | get_pdf_bookmarks | Extract the hierarchical table of contents, bookmarks, and document outline with target page links |
| PDF Forms | get_pdf_form_fields | Read interactive AcroForm form fields (text boxes, checkboxes, radio buttons, dropdowns) and their values |
| PDF Assets | extract_pdf_attachments | Extract embedded file attachments from PDF documents and save them to disk |
| PDF Links | get_pdf_hyperlinks | Extract all clickable web URLs, internal page jumps, and external document links with bounding boxes |
Instead of reading full files (~2500 tokens each), the agent gets back only the matching lines (~110 tokens) — saving up to 98% of context window per query. For files that must be read in full, use compress_code first to strip whitespace overhead and save an additional 10–20% of tokens. For PDF documentation, technical manuals, and specifications, dedicated tools extract targeted text, images, and tables without overwhelming the model context window.
If you have the .NET SDK installed (recommended for .NET developers):
dotnet tool install -g PuneethDC.DotNetSearch
Once installed, the command dotnetsearch is available globally from any terminal.
To update to the latest version in the future:
dotnet tool update -g PuneethDC.DotNetSearch
Go to the latest release and download the self-contained executable for your platform:
| Platform | File to download |
|---|---|
| Windows x64 | DotNetSearch-win-x64.exe |
| Linux x64 | DotNetSearch-linux-x64 |
| macOS ARM64 | DotNetSearch-osx-arm64 |
No .NET installation required. The runtime is bundled inside the executable.
Save it somewhere permanent, e.g.:
C:\Tools\DotNetSearch.exe/usr/local/bin/DotNetSearchOn Linux/macOS, make it executable:
chmod +x /usr/local/bin/DotNetSearch
Open (or create) your global MCP config file:
| Client | Config file location |
|---|---|
| Visual Studio | %USERPROFILE%\.mcp.json |
| VS Code + Copilot | %USERPROFILE%\.mcp.json |
| Claude Desktop | %APPDATA%\Claude\claude_desktop_config.json |
| Cursor | %USERPROFILE%\.cursor\mcp.json |
Works identically across Windows, Linux, and macOS without path differences:
{
"servers": { "DotNetSearch": { "type": "stdio", "command": "dotnetsearch" } } }
{
"servers": { "DotNetSearch": { "type": "stdio", "command": "C:\\Tools\\DotNetSearch.exe" } } }
{
"servers": { "DotNetSearch": { "type": "stdio", "command": "/usr/local/bin/DotNetSearch" } } }
dnx (Zero-Install):{
"servers": { "DotNetSearch": { "type": "stdio", "command": "dnx", "args": ["PuneethDC.DotNetSearch"] } } }
If you already have other servers in the file, just add the "DotNetSearch" block inside the existing "servers" object.
Visual Studio / VS Code:
search, search_related, decompile_type, compress_code, read_fileread_pdf_text, get_pdf_info, render_pdf_page_as_image, extract_pdf_images, get_pdf_bookmarks, get_pdf_form_fields, extract_pdf_attachments, get_pdf_hyperlinksTo make Copilot automatically prefer DotNetSearch over built-in file reading, add the instructions below to your Copilot instructions file. There are two places you can put this — repo-level (affects only that repo) or global (affects all repos).
File location:
<your-repo-root>\.github\copilot-instructions.md
This file is read by GitHub Copilot for every conversation in that repository. It is the recommended place if you want DotNetSearch to be preferred only for a specific project.
How to find or create it:
.github subfolder — if it doesn't exist, create it.github, look for copilot-instructions.md — if it doesn't exist, create the fileFile location:
| Client | Global instructions file |
|---|---|
| Visual Studio | %APPDATA%\Microsoft\VisualStudio\Copilot\copilot-instructions.md |
| VS Code + Copilot | %USERPROFILE%\.github\copilot-instructions.md |
How to find or create it:
copilot-instructions.md doesn't exist in the folder, create the fileNote: If you add instructions to both files, Copilot merges them — both will be active at the same time.
Paste the following into whichever file you chose above:
## Search Preference
When searching for code, symbols, or text in this repository, always prefer the DotNetSearch MCP tool (search and search_related) over reading full files or using built-in workspace search. DotNetSearch returns only matching lines and is significantly more token-efficient (~98% fewer tokens per query). Do not call DotNetSearch tools in parallel — run one search at a time.
When reading, analyzing, or searching PDF documents:
read_pdf_text and get_pdf_info over raw file operations.get_pdf_info to inspect total pages and document metadata.read_pdf_text with specific page ranges (startPage, endPage) to avoid token overflow.render_pdf_page_as_image to rasterize the page.get_pdf_form_fields to extract user inputs.When the user asks about a third-party or NuGet type and the source code is not available in the workspace, use the DotNetSearch MCP tool decompile_type to decompile the type from the project's referenced assemblies. Pass the short or fully qualified type name and the absolute path to the .csproj file. The project must have been built at least once before calling this tool.
When reading source files to minimize context window usage, use the DotNetSearch MCP tool compress_code — BUT ONLY for the following fully-safe languages where indentation is cosmetic and braces/keywords define scope:
C#, Java, JavaScript, TypeScript, C, C++, Kotlin, Swift, Rust, Scala, PHP, Ruby, CSS, SCSS, Less, JSON, XML
NEVER call compress_code on any other language. In particular, NEVER use it on Python, YAML, CoffeeScript, Pug/Jade, HAML, or Makefile — these are indentation-sensitive and compression WILL silently corrupt the file structure.
Before calling compress_code, check the file extension:
Pass the absolute file path. For JavaScript/TypeScript projects using 2-space indentation, set sourceIndentSize=2.
No special commands or workflow changes are needed. Just continue your normal coding tasks in Copilot Agent Mode — DotNetSearch will be used automatically whenever Copilot needs to search your codebase or inspect PDF documents.
Over time you will notice a reduction in token usage per conversation. This is because DotNetSearch returns only the matching lines (typically ~110 tokens) instead of full file contents (~2500 tokens), saving up to 98% of context window per search operation.
searchSearches a local directory for files whose content matches a keyword or natural-language query. Uses BM25 ranking with CamelCase and snake_case token expansion.
| Parameter | Type | Default | Description |
|---|---|---|---|
query | string | required | Keyword, identifier, or natural-language phrase |
repo | string | required | Absolute or relative path to the directory to search |
top_k | int | 5 | Max results to return (1–100) |
context_lines | int | 0 | Lines of context above/below each match (0–5) |
search_relatedFinds code chunks structurally similar to a specific location in a file. Use after search to explore related implementations, callers, or patterns.
| Parameter | Type | Default | Description |
|---|---|---|---|
file_path | string | required | Path to the file as returned by a prior search result |
| line | int | required | Line number (1-indexed) from a pr
只显示了开头部分;其余内容请在仓库中阅读