Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Agent-optimized MCP server with structured JSON tools. 20-45% token savings.
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 …”。
链接指向作者自己的文件;我们不托管也不重新打包它。打开文件后,Claude Desktop 会进行安装。
在终端中运行这条命令
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。
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6日
注册名称已通过 GitHub 账户 beardfaceguy 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(0.1.5)于19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 MCPB。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Symantec Cloud DLP AIops
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Governed Symantec Cloud DLP — 14 MCP tools, read-only with audit.
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。
An agent-optimized OS layer that makes AI coding agents faster and cheaper.
Daimonos replaces the built-in file, search, exec, and git tools in your AI coding agent with structured equivalents that return compact JSON instead of raw terminal output. The result: fewer tokens consumed, fewer round-trips, and lower API costs — with zero changes to your workflow.
Platforms: Linux (x86_64, aarch64) and macOS (Apple Silicon, Intel). Windows support is planned.
For repository agent/operator conventions, see AGENTS.md (especially Daimonos tool usage policy).
The name comes from Greek *daimon* (agent/spirit), the etymological root of "daemon."
When an AI agent runs cargo test, it gets back hundreds of lines of terminal output — progress bars, compile messages, passing test names — when all it needs is "47 passed, 0 failed." The agent pays for every token of that noise: reading it, reasoning about it, and carrying it in context for the rest of the session.
The same waste happens with git status, docker ps, ls -la, and every other shell command. Agents spend 30-50% of their token budget on verbose, unstructured tool output.
Daimonos runs as an MCP server that your IDE or CLI spawns automatically. It provides the same operations agents already use — read files, write files, search, execute commands, git operations — but returns compact, structured JSON instead of raw text.
The single binary also provides ACP, one-shot agent, interactive chat, and socket-daemon runtimes — a full coding-agent harness in its own right; see Agent harness features below and Runtime modes for the explicit subcommands and compatibility aliases.
Agent: exec("cargo test")
Without Daimonos (raw terminal output): Compiling inventory v0.1.0 (/workspace) Finished test profile [unoptimized + debuginfo] target(s) in 2.31s Running unittests src/main.rs (target/debug/deps/inventory-abc123) running 47 tests test config::tests::test_default ... ok test config::tests::test_load ... ok ... (200+ more lines) test result: ok. 47 passed; 0 failed; 0 ignored
With Daimonos (structured JSON): {"ok":true,"tests":47,"passed":47,"failed":0,"failures":[]}
git, cargo, gh, and docker are exposed as first-class MCP tools with structured JSON output. When agents call exec("cargo test"), Daimonos intercepts it and routes through the native plugin instead.{"unchanged":true} instead of the full content), compact field names, lazy tool exposure, batch operations, and a terse output directive that cuts LLM prose by ~30%.Tested with Claude Opus 4.6 on identical coding tasks (read files, search code, edit, run tests, git operations):
| Metric | Baseline | Daimonos | Savings |
|---|---|---|---|
| Output tokens | 5,842 | 3,198 | -45.3% |
| Total tokens | 41,239 | 33,847 | -17.9% |
| Tool calls | 17 avg | 14 avg | -17.6% |
| Wall time | 42.1s avg | 35.2s avg | -16.4% |
Remote benchmarks on AWS (same hardware, same model, same tasks) showed 20.3% cost reduction and 14.0% faster task completion.
Five instances from swe-bench-verified-mini, same model (Claude Opus 4.8) across all three harnesses, each agent running inside the official SWE-bench Docker image for its instance (real test environment), scored with the official swebench evaluation harness:
| Instance | daimonos tokens (LLM calls) | mini-swe-agent tokens (LLM calls) | cursor-agent tokens |
|---|---|---|---|
| django__django-11815 | 54,755 (4) | 38,305 (9) | 113,242 |
| django__django-12155 | 53,773 (4) | 33,095 (9) | 348,425 |
| django__django-12708 | 88,765 (6) | 196,492 (22) | 451,349 |
| sphinx-doc__sphinx-8035 | 172,729 (10) | 342,108 (30) | 930,495 |
| sphinx-doc__sphinx-9367 | 66,812 (5) | 18,611 (6) | 230,223 |
| Total tokens | 436,834 | 628,611 | 2,073,734 |
| Total wall time | 84 s | 247 s | 279 s |
| Resolved | 5/5 | 5/5 | 5/5 |
Conclusions:
See benchmarks/swebench/ for the runners and methodology.
Use this script for README readers, release notes, and social posts:
# 1) Install daimonos
cargo build --release sudo cp target/release/daimonos /usr/local/bin/
What to highlight in the demo:
Pre-built binaries (Linux and macOS):
# Linux x86_64
curl -L https://github.com/beardfaceguy/daimonos/releases/latest/download/daimonos-x86_64-linux.tar.gz | tar xz sudo mv daimonos /usr/local/bin/
curl -L https://github.com/beardfaceguy/daimonos/releases/latest/download/daimonos-aarch64-macos.tar.gz | tar xz sudo mv daimonos /usr/local/bin/
From source:
git clone https://github.com/beardfaceguy/daimonos.git
cd daimonos cargo build --release sudo cp target/release/daimonos /usr/local/bin/
See docs/install.md for all platforms (ARM Linux, Intel Mac, musl static builds).
For most users, start with one of these:
Add Daimonos as an MCP server. For Cursor, add to your project's .cursor/mcp.json:
{
"mcpServers": { "daimonos": { "command": "daimonos", "args": ["--mcp", "-w", "/path/to/your/project"] } } }
That's it. Daimonos starts when your IDE opens the project and exits when you close it. No daemon to manage, no background service.
| Tool | What it does |
|---|---|
read_file | Read with optional offset/limit, content-hash deduplication |
write_file | Write with auto-mkdir |
edit_file | String replacement with diff confirmation |
search | Regex search (content mode) or file discovery (file mode) |
exec | Run commands with semantic filtering, bounded capture, and owned teardown |
batch | Multiple operations in a single round-trip |
workspace_info | Project type, git status, directory listing, analytics |
These appear automatically when the corresponding CLI tool is found on PATH:
| Plugin | Commands | Detected by |
|---|---|---|
git | status, log, diff, branch, add, commit, push, pull, checkout | .git directory |
git_read | resolve, show_file, list_tree, grep, log, show_commit, diff, blame, refs, ancestry, worktree_status | git with --no-lazy-fetch support |
cargo | test, build, check, clippy, fmt, add | Cargo.toml |
gh | pr_view, pr_list, pr_create, pr_diff, pr_checks, api | gh on PATH |
docker | ps, logs, exec, images, inspect, stop, compose_up/down/ps | docker on PATH |
daimonos --stats)Raw exec, background jobs, and CLI plugins (cargo, git, gh, docker, npm, pytest, curl, and shellcheck) share one managed execution layer:
0600 files under a private 0700 directory, with configurable byte and job-count limitsBeyond the MCP server, the same binary is a complete coding-agent harness: an interactive terminal UI (daimonos agent), an ACP backend for Zed, a one-shot CLI, and a session daemon with attach/detach and remote control.
Many of its recent features come from a systematic study of 60+ open-source agent harnesses (Ai
只显示了开头部分;其余内容请在仓库中阅读