GitHub
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
Computer or cloud
Log Detective MCP
Overview
Description from the maker (original English)
MCP server providing log snippet and Python traceback extraction tools.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiGrowing↓ 3.2K/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Developer tools
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"logdetective": {
"command": "uvx",
"args": [
"logdetective-mcp"
]
}
}
}Run this in your terminal
claude mcp add logdetective -- uvx logdetective-mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"logdetective": {
"command": "uvx",
"args": [
"logdetective-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"logdetective": {
"type": "stdio",
"command": "uvx",
"args": [
"logdetective-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account fedora-copr, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Developer toolsi
- More stars than 0% of the other 5555 servers in this category
- Published to the registry more recently than 75% of the other 5555 servers in this category
- Basic checks equal to or better than 100% of the other 5555 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Log Detective MCP safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.7.0) reached the registry 6 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Desktop Commander
MCP server for terminal commands, file operations, and process management
Runs on your computer
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Log Detective MCP
MCP server implementing core log analysis tools of Log Detective for use by other agents.
The server uses the Drain3 algorithm to cluster log messages into templates and extract a representative subset of snippets, reducing large logs to a manageable size for downstream analysis.
Installation
Requires Python 3.11+.
pip install .Or with uv:
uv pip install .Usage
Running the server
logdetective-mcpOr directly:
python -m logdetective_mcp.mainMCP client configuration
Claude Code
claude mcp add logdetective -- logdetective-mcpClaude Desktop
Add to your Claude Desktop configuration file:
{"mcpServers": { "logdetective": { "command": "logdetective-mcp" } } }
Tools
extract_log_snippets
Extracts representative log snippets using Drain3 clustering. The tool chunks the log into logical messages, clusters similar messages, and returns one representative snippet per cluster.
Log content can be provided in three ways (exactly one must be used):
| Parameter | Type | Description |
|---|---|---|
log_text | str | Raw log text passed directly. |
log_path | str | Path to a log file on the server's filesystem. |
log_url | str | HTTP(S) URL to fetch log content from. |
The input schema for both tools uses oneOf to require exactly one nonempty source field. Omitted source fields and fields set to "" do not count as a source. This matches the rule checked when a tool runs. The advertised tool definitions conform to the MCP 2025-11-25 Tool schema.
Optional parameters:
| Parameter | Type | Default | Description | |
|---|---|---|---|---|
max_clusters | int | 8 | Maximum number of snippets to extract. | |
max_snippet_len | int | 2000 | Maximum character length per snippet. | |
skip_patterns | `list[str] \ | null` | null | List of regex patterns. Chunks matching any pattern are excluded before clustering. |
context | int | 0 | Number of lines to include before and after each snippet (0-50). |
Returns a list of Snippet objects, each with line_number (position in the original log) and text (the extracted snippet content). MCP structured output wraps this list as {"result": [...]}; the advertised output schema describes that object.
extract_python_tracebacks
Extracts Python tracebacks from the log, including chained exceptions.
| Parameter | Type | Default | Description | |
|---|---|---|---|---|
max_traceback_len | int | 2000 | Maximum character length per extracted traceback. | |
skip_patterns | `list[str] \ | null` | null | List of regex patterns. Tracebacks matching any pattern are excluded. |
context | int | 0 | Number of lines to include before and after each traceback (0-50). |
For both extractor tools, context follows grep's -C behavior: the requested number of surrounding log lines is included on each side when available. The length limit applies to the extracted snippet or traceback; context lines are additional, so the returned text can be longer than the limit. With context enabled, line_number points to the first returned line. Values outside 0-50 are rejected.
If a traceback is truncated, the extractor keeps its beginning and end with a marker when max_traceback_len permits; smaller limits keep only a prefix. Following context starts after the original traceback, without restoring its omitted middle. If a log line is split into chunks by max_snippet_len, each selected chunk keeps its own text when context is added. Neighboring lines do not replace it with the complete long line. Context boundaries follow the chunk's original source lines, including blank lines omitted from the rendered chunk.
Size limits
All downloaded inputs (via log_url) are limited to 100 MB regardless of format. Downloads are streamed in chunks and aborted as soon as the limit is exceeded.
Archives read via log_path are also checked against the 100 MB limit during decompression.
Compressed file support
Both tools transparently decompress archived log files. Supported formats:
- Single-file compression:
.gz,.bz2,.xz,.lzma - Archives:
.zip,.tar,.tar.gz,.tar.bz2,.tar.xz
Format is detected from the file extension (or URL path). Archives must contain exactly one file.
Zip bomb protection is enforced via:
- Maximum decompressed size of 100 MB
- Maximum compression ratio of 100:1
- Pre-check of declared sizes in zip/tar headers
- No recursive decompression (nested archives are not unpacked)
Contributing
All changes to this repository must pass pre-commit checks and tests.
uv run pytest tests/To inspect the JSON schemas returned by the installed server, run the check script with the project's virtual environment. It starts the server over MCP stdio, validates both tool schemas and structured results, and compares the returned tool definitions with the files in schemas/:
.venv/bin/python scripts/check_mcp_schemas.pyAfter an intentional schema change, regenerate those files with:
.venv/bin/python scripts/check_mcp_schemas.py --write