AI Skill Hub 强烈推荐:ctx开源MCP工具 是一款优质的MCP工具。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。
ctx开源MCP工具 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。
ctx开源MCP工具 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/stevesolun/ctx
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"ctx--mcp--": {
"command": "npx",
"args": ["-y", "ctx"]
}
}
}
# 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 ctx开源MCP工具 执行以下任务... Claude: [自动调用 ctx开源MCP工具 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": {
"ctx__mcp__": {
"command": "npx",
"args": ["-y", "ctx"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Find the cheapest AI coding setup that actually works on your repo.
CTX Fit analyzes your repository, tests promising AI coding configurations against real tasks in it, and produces the winning configuration as a reviewable change — in your working tree with --apply, or as a pull request with --pr. It picks the cheapest setup that reliably works — reliability is a requirement, not a tie-break — and if nothing beats what you already have, it says so.
The winner is chosen by a fixed rule, not a score: discard every candidate below the reliability floor, then minimize attributable cost, then break ties toward the simpler configuration. An LLM may explain a result; it never decides one.
Release scope (1.0.21). CTX Fit compares capability configurations within one coding-agent harness; it does not compare Codex, Claude Code, or other harnesses against one another. It recognizes and can run repository-native verification commands for Python, JavaScript/TypeScript, Go, Rust, and Make, and treats the selected test command as the verification authority. For an installable Python project, CTX Fit builds a campaign environment and installs it without network access; its build backend and dependencies must already be available without downloading them. In the other ecosystems, verification is supported only when the runtime is usable from the hostPATHunder an isolated home and the verification dependencies are already available in the repository. Final verification uses that isolated home and runs without network access, so a user's package caches are not a supported dependency source. This is evidence for normal development; it does not prove that deliberately hostile code cannot deceive its own test runner. Release qualification did not include a paid live-provider trial, so inspectctx doctorand the dry run before authorizing spend.
pip install --upgrade claude-ctx
cd /path/to/my-project
ctx fit
Bare ctx fit is free, local and read-only: it runs no model, spends nothing, and issues no git commands at all. Example output, abridged from a real run against this repository:
Repository: /path/to/ctx
Languages: python, javascript
Current AI coding setup
Instructions: AGENTS.md, CLAUDE.md
Tool config: .claude/settings.local.json
Installed skills: 26
How this repository verifies itself
test python -m pytest -q
from pyproject.toml [tool.pytest] (high confidence)
typecheck python -m mypy src
from pyproject.toml [tool.mypy] (high confidence)
lint python -m ruff check .
from pyproject.toml [tool.ruff] (high confidence)
build python -m build
from pyproject.toml [build-system] (medium confidence)
AI agent readiness
91/100
Verification 30/30
Instructions 20/20
Environment 6/15
CI enforcement 15/15
Tool safety 10/10
Context tractability 10/10
Highest-impact improvements
1. Commit a dependency lockfile. (+9)
no dependency lockfile is committed
This repository has the static evidence needed to plan an evaluation: it
declares deterministic tests. Whether those tests can execute is checked only
inside the campaign.
Requires Python 3.11 or newer. Add --json for machine-readable output, or --dry-run to see what a full evaluation would involve. --dry-run does read your history — it runs read-only git queries (log, show --name-only, ls-tree, rev-parse) to derive representative tasks — and writes nothing: not to the repository, not to the index, not to any ref.
Beyond the free profile, ctx fit --test --budget N evaluates candidate configurations against those tasks. Spending needs both flags: --test without --budget only plans. Run ctx doctor to see whether a real evaluation can run here. A real evaluation needs pip install "claude-ctx[harness]", Node.js with npx for the workspace-filesystem MCP, a matching provider credential, and Bubblewrap on Linux; the base install can profile, plan, and simulate. Without a matching provider credential, --test runs in simulation, which proves the pipeline but not your repository. With a credential but a missing live prerequisite, CTX refuses the run before trial setup. A simulated result is refused as evidence for --apply and --pr.
Ubuntu 24.04 restricts unprivileged user namespaces, and merely installing bwrap does not prove it can start the network-disabled namespace CTX uses for repository commands. Install and load Ubuntu's packaged, scoped bwrap-userns-restrict profile for /usr/bin/bwrap:
sudo apt update
sudo apt install bubblewrap apparmor-profiles apparmor-utils
if [ ! -e /etc/apparmor.d/bwrap-userns-restrict ]; then
sudo install -m 0644 \
/usr/share/apparmor/extra-profiles/bwrap-userns-restrict \
/etc/apparmor.d/bwrap-userns-restrict
fi
sudo apparmor_parser -r /etc/apparmor.d/bwrap-userns-restrict
ctx doctor
Keep Ubuntu's global unprivileged-user-namespace restriction enabled; CTX uses the targeted Bubblewrap profile instead of weakening that system-wide security boundary. The profile is administrator-visible host policy for every /usr/bin/bwrap caller, not a CTX-private setting; the commands above preserve an existing local profile rather than overwriting it. ctx doctor proves this path with a bounded /bin/true probe in the same no-network namespace. It executes no repository code and calls no model. See Ubuntu's AppArmor user-namespace guidance and the packaged Bubblewrap profile.
Requires CPython 3.11 or newer. Linux and macOS are the tested host platforms; other POSIX systems are best-effort. Native Windows and PowerShell are not supported. On a Windows machine, run ctx inside WSL2 as a Linux installation.
pip install claude-ctx
For real model-backed evaluations, install claude-ctx[harness] instead and make Node.js plus npx available. Linux hosts also need Bubblewrap. ctx doctor checks these prerequisites without contacting a model or spending money.
Version 1.0.21 ships ctx fit as the primary command, plus ctx doctor and ctx advanced. The established agent-loop spellings (ctx run, ctx resume, ctx sessions) remain supported.
| Tracker ID | User outcome |
|---|---|
CLI-002 | Scan a repository and receive a bounded skill, agent, and MCP recommendation set. |
CLI-026 | Review a custom-model harness recommendation with python -m harness_install <slug> --dry-run before installation. The slug is required: --dry-run on its own exits 2. |
API-011 | Manage local entities through the dashboard's validated API. |
<details> <summary>Tracking sources</summary>
Release readiness is tracked in qa/feature_status.csv. The docs/qa/feature-user-story-status.csv, docs/qa/dashboard-user-story-status.csv, and qa/tool-selection-token-history/tracker.csv files are supporting detail ledgers.
</details>
| Task | CLI | Guide |
|---|---|---|
| Profile a repository for AI coding readiness | ctx (same as ctx fit) | this README |
| Evaluate candidate configurations and pick a winner | ctx fit --test --budget N | this README |
| Write the winner into the working tree | ctx fit --apply | this README |
| Open a pull request with the winner | ctx fit --pr | this README |
| Diagnose whether a real evaluation can run here | ctx doctor | this README |
| Initialize the recommendation surface and install graph data | ctx-init | [Knowledge graph](https://stevesolun.github.io/ctx/knowledge-graph/) |
| Scan a repository for skill/agent/MCP recommendations | ctx-scan-repo | [Entity onboarding](https://stevesolun.github.io/ctx/entity-onboarding/) |
| Connect an MCP, Python, or CLI host | ctx-mcp-server, ctx advanced run | [Host integration](https://stevesolun.github.io/ctx/harness/attaching-to-hosts/) |
| Inspect the local recommendation runtime | python -m ctx_monitor serve | [Dashboard](https://stevesolun.github.io/ctx/dashboard/) |
| Review or export telemetry | ctx-telemetry-export, ctx-telemetry-retention | [Telemetry](https://stevesolun.github.io/ctx/telemetry/) |
This table describes release 1.0.21. Bare ctx with no arguments runs the Fit profile, which is why it is not listed under the recommendation surface.
The agent-loop harness (run, resume, sessions) is still there and still supported. It moved under ctx advanced so the top-level help stays about the product, but the original spellings keep working: ctx run ... and ctx advanced run ... are the same command, and ctx run --help still prints the harness options. Only ctx --help changed — it advertises fit, doctor and advanced. Maintenance utilities that used to be console scripts are reached with python -m — for example python -m ctx.cli.recommend or python -m ctx.core.quality.dedup_check.
See the full documentation for configuration, APIs, entity lifecycle, and operational details.
高质量的开源MCP工具,提供丰富的功能
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。
总体来看,ctx开源MCP工具 是一款质量优秀的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | ctx |
| 原始描述 | 开源MCP工具:Skill, agent, MCP, and harness recommendations for Claude Code/custom LLMs: 102,。⭐524 · Python |
| Topics | mcpai-agentsanthropicautomation |
| GitHub | https://github.com/stevesolun/ctx |
| License | MIT |
| 语言 | Python |
收录时间:2026-06-22 · 更新时间:2026-06-23 · 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