unstuck-mcp
Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.
README
unstuck-mcp
Stops coding agents from looping on the same failed fix. Tracks failed attempts per-problem, and once the same error has been "fixed" a couple of times without success, it blocks further fixes until the agent actually uses its own web search tool — instead of guessing again from stale memory.
Works with GitHub Copilot CLI, OpenAI Codex CLI, and OpenCode, because
all three speak MCP, all three read AGENTS.md, and all three already ship a
web search tool of their own. This server doesn't duplicate that search — it
just gates on whether it happened.
How it works
- The agent calls
report_attempt(error_signature, fix_summary)after any failed fix. - The server normalizes the error text (strips line numbers, file paths, quoted values) and hashes it, so near-identical failures are recognized as "the same problem" even if the wording drifts slightly.
- On the 3rd attempt at the same normalized problem (configurable), the tool response tells the agent it's blocked: it must use its own web search tool before trying anything else.
- Once it has searched, the agent calls
confirm_search(error_signature, findings_summary). This clears the block and resets the attempt count for that problem, informed by what it just found. - If the agent tries another fix while still blocked (i.e. it skipped the
search),
report_attemptcalls it out explicitly instead of quietly logging it.
This is enforced through instructions, not a hard runtime block — MCP servers
can't intercept an agent's other tool calls the way an editor plugin can. It
works because the tool descriptions and the bundled AGENTS.md rule make
calling it the obvious, expected thing to do, the same pattern that made
docs-lookup MCP servers like Context7 stick. confirm_search closes the loop:
the agent can't just claim it's unblocked without a tool call that says so.
Install
cd unstuck-mcp
npm install
Then wire it into whichever CLI(s) you use:
GitHub Copilot CLI
copilot mcp add
# Command: node
# Args: /absolute/path/to/unstuck-mcp/index.js
or edit ~/.copilot/mcp-config.json directly:
{
"mcpServers": {
"unstuck": {
"command": "node",
"args": ["/absolute/path/to/unstuck-mcp/index.js"]
}
}
}
OpenAI Codex CLI
Add to your Codex config (~/.codex/config.toml):
[mcp_servers.unstuck]
command = "node"
args = ["/absolute/path/to/unstuck-mcp/index.js"]
OpenCode
Add to opencode.json (project or global):
{
"mcp": {
"unstuck": {
"type": "local",
"command": ["node", "/absolute/path/to/unstuck-mcp/index.js"],
"enabled": true
}
}
}
Enable the enforcement rule
Copy the contents of AGENTS.snippet.md into your project's AGENTS.md
(all three tools read this file). Without it, the agent still can call these
tools, but won't reliably choose to on its own.
Configuration
Environment variables (set them in the MCP server config's env block):
| Variable | Default | Purpose |
|---|---|---|
UNSTUCK_THRESHOLD |
2 |
How many failed attempts before blocking (3rd attempt = 1st time blocked) |
UNSTUCK_DIR |
<cwd>/.unstuck |
Where attempt history is stored |
Tools exposed
report_attempt(error_signature, fix_summary)— log a failed attempt, get back whether you're blockedconfirm_search(error_signature, findings_summary)— clear a block after actually searchingloop_status()— see everything currently tracked as stuck, for debuggingreset_loop(error_signature?, clear_all?)— clear history manually
Known limitation
This relies on the agent following its instructions honestly — calling
report_attempt after failures, and not calling confirm_search without
actually searching. It's not a hard sandbox-level block like an OpenCode
plugin hook could be. In exchange, it works identically across every
MCP-compatible agent instead of being locked to one tool's plugin architecture,
and it doesn't duplicate a search tool the agent already has.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。