log-probe-mcp
Enables MCP-compatible coding agents to debug applications using runtime log data, by providing tools to start a local log-ingestion server, track debugging sessions and hypotheses, and correlate logs to specific executions.
README
log-probe-mcp
An agent-agnostic MCP server for hypothesis-driven, runtime-data-backed debugging.
Point any MCP-compatible coding agent (Claude Code, Cursor, etc.) at it and say something like:
Debug this using the log-probe mcp — checkout returns the wrong cart for some users.
log-probe-mcp never edits your code. Instead it runs a local HTTP log-ingestion server, correlates incoming logs to specific executions (a script run, a test invocation, a request), and tracks hypotheses — so the calling agent can drive the classic debugging loop (form hypotheses → instrument → reproduce → analyze → converge) with real runtime data instead of guessing from static code, and without a human manually copy-pasting console output back into the chat.
How it works
- The agent calls
probe_server_startto spin up a local HTTP server that accepts structured log events (POST /ingest). - It records a debugging session and a few falsifiable hypotheses (
debug_session_create,hypothesis_create). - It fetches the logging contract (
instrumentation_get_contract) and inserts a few log calls at the decision points that would distinguish between the hypotheses, using its own file-editing tools — log-probe-mcp only tells it what to send and where to send it, it never touches your source files itself. - It reproduces the bug, either by running a script/test itself (
execution_run, which also captures stdout/stderr automatically) or by minting an execution for something already running (execution_create) and asking a human to trigger it. - It reads the real data back (
execution_get_logs,execution_comparefor flaky/intermittent bugs) and marks each hypothesis confirmed/refuted with evidence (hypothesis_update). - Once resolved, it applies the actual fix itself, removes the temporary instrumentation, and
optionally writes a durable record (
knowledge_base_export,debug_session_resolve).
Call debug_workflow_guide (or use the debug MCP prompt, on clients that support prompts) for
the full step-by-step guidance an agent needs to run this loop well.
Installation / client config
{
"mcpServers": {
"log-probe": {
"command": "npx",
"args": ["-y", "log-probe-mcp"]
}
}
}
For local development against a checkout of this repo, build it and point a client directly at
dist/bin.js:
{
"mcpServers": {
"log-probe": {
"command": "node",
"args": ["/absolute/path/to/log-probe-mcp/dist/bin.js"]
}
}
}
Data (the SQLite store and any exported knowledge-base files) lives under .log-probe/ in the
project the agent is working in. Because MCP clients don't consistently launch servers with cwd
set to the project root, the authoritative source is, in priority order: the dataDir argument to
probe_server_start, the LOG_PROBE_DATA_DIR environment variable, then the server process's own
cwd.
Tool surface
| Tool | Purpose |
|---|---|
probe_server_start / probe_server_stop / probe_server_status |
Ingestion server lifecycle |
debug_session_create / _list / _get / _resolve |
Track a debugging investigation |
hypothesis_create / _update / _list |
Track falsifiable hypotheses and their evidence |
execution_create / _run / _end / _list / _get_logs / _compare |
Mint/run/query correlated executions |
instrumentation_get_contract |
The ingestion HTTP contract + ready-to-paste snippets per language |
debug_workflow_guide |
The hypothesis-driven methodology, full guide or per-stage |
knowledge_base_export |
Writes a durable markdown record of a session |
Plus a debug MCP prompt for clients that support the prompts primitive — a thin wrapper around
debug_workflow_guide's content, so guidance is reachable via tools everywhere regardless of
prompt support.
Ingestion contract
Instrumented code sends a POST to <ingestion url>/ingest with a JSON body (single event, or an
array of up to 500 for batching):
{
"executionId": "exec_...",
"hypothesisId": "hyp_...",
"level": "info",
"message": "cache key computed",
"data": { "key": "route:/x" },
"source": "checkout.ts:88"
}
executionId must already exist (minted via execution_create or execution_run) — this is what
correlation is built on. Instrumentation should always be fire-and-forget with a short timeout; see
instrumentation_get_contract for language-specific snippets that already do this correctly.
Known limitations
- The ingestion server binds
127.0.0.1only and has no auth token — acceptable for a local dev tool, but don't run it anywhere multi-tenant or expose the port. - One MCP server process serves one data directory for its lifetime; to point at a different
project, restart/reconnect the client rather than changing
dataDirmid-session.
Example
examples/buggy-node-service/ is a small, intentionally-buggy HTTP server for trying the full
workflow end to end — see its README.
Development
npm install
npm run build # compiles to dist/ and copies the SQL migration
npm run dev # tsx watch, for iterating
npm run typecheck
npm test
npm run inspect # build + launch the MCP inspector against dist/bin.js
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。