经 AI Skill Hub 精选评估,系统MCP常用器 获评「推荐使用」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。
系统MCP器为一个合送用户代球Claude Code,Codex,Gemini,Grok,Mist。定义用户代球中心系统一个系统器
系统MCP常用器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
系统MCP器为一个合送用户代球Claude Code,Codex,Gemini,Grok,Mist。定义用户代球中心系统一个系统器
系统MCP常用器 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/verivus-oss/llm-cli-gateway
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"--mcp---": {
"command": "npx",
"args": ["-y", "llm-cli-gateway"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 系统MCP常用器 执行以下任务... Claude: [自动调用 系统MCP常用器 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"__mcp___": {
"command": "npx",
"args": ["-y", "llm-cli-gateway"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
"Without consultation, plans are frustrated, but with many counselors they succeed." — Proverbs 15:22 (LSB)
A Model Context Protocol (MCP) gateway for running Claude Code, Codex, Gemini, Grok, and Mistral (Vibe) CLIs from one MCP endpoint, with durable async jobs, session continuity, cache-aware prompting, observability, and personal-appliance setup tooling.
Why developers try it: one local MCP endpoint for cross-LLM validation, multi-agent coding workflows, and repeatable assistant-led setup across five provider CLIs.
Current signals: CI and security workflows pass on main, OpenSSF Scorecard is published, OpenSSF Best Practices is passing, releases use Sigstore signing, and the package is MIT licensed.
GEMINI_MODELS='{"gemini-team-default":"Team-approved Gemini model"}'
Node.js >= 24.4.0 is required (engines.node in package.json). The gateway uses Node's built-in node:sqlite module for persistence — there is no native binding to compile and no install scripts run. The 24.4 floor is where allowBareNamedParameters defaults to true, which the persistence layer relies on.
Before using this gateway, you need to install the CLI tools you want to use:
Windows PowerShell:
$Version = '<version>'
$Base = "https://github.com/verivus-oss/llm-cli-gateway/releases/download/v$Version"
$InstallDir = Join-Path (Join-Path $env:LOCALAPPDATA 'Programs') 'llm-cli-gateway'
$ExeName = "llm-cli-gateway-$Version-windows-amd64.exe"
$BundleName = "llm-cli-gateway-bundle-$Version-windows-amd64.tar.gz"
$Exe = Join-Path $InstallDir 'llm-cli-gateway.exe'
$Checksums = Join-Path $InstallDir 'SHA256SUMS'
$ChecksumBundle = Join-Path $InstallDir 'SHA256SUMS.sigstore.json'
New-Item -ItemType Directory -Force $InstallDir | Out-Null
Invoke-WebRequest -UseBasicParsing "$Base/$ExeName" -OutFile $Exe
Invoke-WebRequest -UseBasicParsing "$Base/SHA256SUMS" -OutFile $Checksums
Invoke-WebRequest -UseBasicParsing "$Base/SHA256SUMS.sigstore.json" -OutFile $ChecksumBundle
cosign verify-blob $Checksums --bundle $ChecksumBundle --certificate-identity "https://github.com/verivus-oss/llm-cli-gateway/.github/workflows/release-installer.yml@refs/tags/v$Version" --certificate-oidc-issuer "https://token.actions.githubusercontent.com"
if ($LASTEXITCODE -ne 0) { throw "Sigstore verification failed for SHA256SUMS" }
function Get-ReleaseSha256($Name) {
$line = Select-String -Path $Checksums -Pattern "^[a-fA-F0-9]{64}\s+$([regex]::Escape($Name))$" | Select-Object -First 1
if (-not $line) { throw "No SHA256SUMS entry found for $Name" }
return (($line.Line -split "\s+")[0]).ToLowerInvariant()
}
if ((Get-FileHash $Exe -Algorithm SHA256).Hash.ToLowerInvariant() -ne (Get-ReleaseSha256 $ExeName)) { throw "Checksum mismatch for $ExeName" }
$env:RVWR_GATEWAY_BUNDLE_URL = "$Base/$BundleName"
$env:RVWR_GATEWAY_BUNDLE_SHA256 = Get-ReleaseSha256 $BundleName
& $Exe setup
& $Exe stop
& $Exe install-bundle
& $Exe start
& $Exe status
& $Exe doctor
The Windows installer keeps a stable llm-cli-gateway.exe command in %LOCALAPPDATA%\Programs\llm-cli-gateway and adds that directory to the user PATH. Do not script against release-versioned exe names after install.
```bash
./llm-cli-gateway-<ver>-<os>-<arch> uninstall ./llm-cli-gateway-<ver>-<os>-<arch> uninstall --yes
Docker fallback:
bash LLM_GATEWAY_AUTH_TOKEN=$(openssl rand -hex 32) \ docker compose -f docker/personal.compose.yml up -d docker compose -f docker/personal.compose.yml run --rm doctor ```
```bash curl -fsSL https://x.ai/cli/install.sh | bash grok login # OAuth flow; for headless auth, set XAI_API_KEY
```
npm install -g llm-cli-gateway
Or use directly with npx from an MCP client:
{
"mcpServers": {
"llm-gateway": {
"command": "npx",
"args": ["-y", "llm-cli-gateway"]
}
}
}
./llm-cli-gateway-<new>-<os>-<arch> upgrade
CODEX_CONFIG_PATH=/path/to/config.toml CLAUDE_SETTINGS_PATH=/path/to/settings.json CLAUDE_SETTINGS_LOCAL_PATH=/path/to/settings.local.json GEMINI_SETTINGS_PATH=/path/to/settings.json GEMINI_HISTORY_ROOT=/path/to/.gemini/tmp
- DEBUG: Enable debug logging (set to any value)
DEBUG=1 node dist/index.js
- LLM_GATEWAY_APPROVAL_POLICY: Default approval policy when request does
retentionDays = 30 dedupWindowMs = 3600000 acknowledgeEphemeral = false # required to enable async tools with memory backend
Backends:
- **`sqlite`** (default) — durable, file-backed. Safe for single-instance deployments.
- **`memory`** — in-process Map. Lost on gateway exit. Requires `acknowledgeEphemeral = true` to be loaded. Suitable for tests and ephemeral CI gateways.
- **`postgres`** — interface only, implementation not yet shipped. Selecting this backend throws at startup.
- **`none`** — no store. **`*_request_async`, `llm_job_status`, `llm_job_result`, and `llm_job_cancel` are NOT registered on the gateway.** This is a structural invariant: agents that try to call async tools against a gateway with `backend = "none"` get a clean "tool not found" at connect time instead of silent in-memory loss after the 1-hour TTL. Use `llm_process_health` to inspect the resolved persistence state programmatically.
Legacy environment variables (deprecated; emit a warning at startup):
- `LLM_GATEWAY_LOGS_DB` / `LLM_GATEWAY_JOBS_DB` — `none` selects `backend = "none"`; any other value selects `backend = "sqlite"` with that path.
- `LLM_GATEWAY_JOB_RETENTION_DAYS` — overrides `retentionDays`.
- `LLM_GATEWAY_DEDUP_WINDOW_MS` — overrides `dedupWindowMs`.
- `LLM_GATEWAY_ACKNOWLEDGE_EPHEMERAL` — `1`/`true`/`yes` sets `acknowledgeEphemeral = true`.
##### Per-project isolation
By default, **all gateway data is global per user**, not per project. With no overrides, every Claude Code window — across every repo — spawns its own gateway subprocess but they all read and write the same files:
- `~/.llm-cli-gateway/logs.db` (async jobs + flight recorder)
- `~/.llm-cli-gateway/sessions.json` (CLI sessions)
- `~/.llm-cli-gateway/config.toml` (resolved config)
This is usually what you want — `session_list` from repo A shows sessions from repo B, an async job started in window A can be polled from window B, and the 1-hour dedup window catches re-issues across windows. SQLite WAL mode makes concurrent access from multiple gateway subprocesses safe.
If you instead want **per-project isolation** (e.g. unrelated repos shouldn't share session lists or risk false dedup hits), point each project at its own config file. In `.claude/settings.local.json` for the project:
json { "mcpServers": { "llm-gateway": { "env": { "LLM_GATEWAY_CONFIG": "${workspaceFolder}/.gateway/config.toml" } } } }
…and put a per-project `config.toml` in the repo:
toml [persistence] backend = "sqlite" path = "/srv/repos/.../my-repo/.gateway/logs.db"
Now every gateway subprocess spawned for _this_ repo's Claude Code window reads its own config and writes to its own SQLite file; sessions, jobs, and dedup state are scoped to the repo. Other repos keep using the global default. `llm_process_health.persistence.sources.configFile` lets an agent confirm which config it's actually running under.
###### Agent-executable spec (DAG-TOML)
If you want an LLM agent to perform this setup deterministically — rather than reading the prose above and guessing — copy the following DAG-TOML into the repo (e.g. `docs/planning/per-project-gateway-isolation.toml`) and point your agent at it. The schema is [`agent-assurance`](https://github.com/verivus-oss/agent-assurance/blob/main/SPEC.md) `template_kind = "implementation-dag"`. The agent MUST execute units in layer order, must not skip the verification unit, and must treat any failed gate as blocking.
toml [meta] schema_version = "1.0.0" template_kind = "implementation-dag" docs = "https://github.com/verivus-oss/agent-assurance/blob/main/SPEC.md" confidentiality = "public" title = "Per-project llm-cli-gateway persistence isolation" spec = "https://github.com/verivus-oss/llm-cli-gateway#per-project-isolation" created = "YYYY-MM-DD" total_units = 5 tier1_units = ["U01","U02","U03","U04","U05"] tier2_units = [] tier3_units = []
The repo ships agent-ready workflow skills under .agents/skills for async orchestration, session continuity, multi-LLM review, implement-review-fix loops, and secure approval-gated dispatch. Machine-readable DAG-TOML plans live under docs/plans and setup/install-plan.dag.toml for workflows that need deterministic sequencing and verification gates.
The next documentation focus is provider-specific skill and DAG-TOML pairs for each outbound CLI: Claude, Codex, Gemini, Grok, and Mistral Vibe. The implementation plan is tracked in docs/plans/provider-workflow-assets.dag.toml, with each provider asset expected to cover install/login checks, session behavior, approval modes, cache/telemetry surfaces, failure modes, and a smoke-test gate.
// 1. Create a new session
await callTool("session_create", {
cli: "claude",
description: "Debugging session",
setAsActive: true,
});
// 2. Make requests (automatically uses active session)
await callTool("claude_request", {
prompt: "What's the bug in this code?",
// sessionId is automatically used
});
// 3. Continue the conversation
await callTool("claude_request", {
prompt: "Can you explain that fix in more detail?",
continueSession: true,
});
// 4. List all sessions
await callTool("session_list", { cli: "claude" });
// 5. Switch to a different session
await callTool("session_set_active", {
cli: "claude",
sessionId: "some-other-session-id",
});
// 6. Delete when done
await callTool("session_delete", {
sessionId: "session-id-to-delete",
});
本项目是 llm-cli-gateway 的 README 文件,提供项目概述、功能特性、环境依赖和系统要求等信息。
本项目提供多个功能特性,包括多 LL 模型协调、会话管理、工作树管理、令牌优化等。
本项目要求 Node.js 版本 >= 24.4.0,使用 Node 的内置 sqlite 模块进行持久化存储。
安装 llm-cli-gateway 可以使用单个二进制文件,支持 Windows、Linux 和 macOS 等操作系统,或者使用 Docker 等容器化方式部署。
使用 llm-cli-gateway 可以通过 npm 或 npx 方式安装和使用,或者直接使用 Docker 等容器化方式部署。
配置 llm-cli-gateway 可以通过环境变量、MCP 等方式进行配置,包括设置令牌、工作树等参数。
API/接口说明
本项目提供多个工作流和模块,包括会话管理、多 LL 模型协调、令牌优化等功能,支持异步协调和会话续传等特性。
系统MCP器为一个合送用户代球中心系统丁个系统器。很给颗直一个系统器。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:系统MCP常用器 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | llm-cli-gateway |
| 原始描述 | 开源MCP工具:MCP server providing unified access to Claude Code, Codex, Gemini, Grok and Mist。⭐8 · TypeScript |
| Topics | mcpclaude-codecodexgemini-cligrokllm |
| GitHub | https://github.com/verivus-oss/llm-cli-gateway |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-06-08 · 更新时间:2026-06-08 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端