trovex runs on your own machine — the client starts it, so there is no endpoint to ping. 547 installs a week from pypi. Last commit 2 Sep 2026.
Serves coding agents one canonical doc per query instead of rereading the repo — ~60% fewer tokens.
We read the source, 17 h ago · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
listed = subprocess.run([claude, "mcp", "list"], capture_output=True, text=True, timeout=15)
<select name="kind" class="ctl-select">
<option value="" {% if not kind %}selected{% endif %}>all</option>
def eval(n: int = 40, k: int = 5) -> None: # noqa: A001
list.innerHTML = data.map(d =>
Found in continuous integration, deployment or infrastructure files, or in a neighbouring package of the same monorepo. None of this is installed when you add the server: it describes how the project is built and released. We list it because a leaked key in a build pipeline is still a real problem, but it is not something this server does on your machine.
PLIST="$HOME/Library/LaunchAgents/$LABEL.plist"
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add trovex -- uvx trovex
{
"mcpServers": {
"trovex": {
"args": [
"trovex"
],
"command": "uvx"
}
}
}
[mcp_servers.trovex]
command = "uvx"
args = ["trovex"]
{
"mcpServers": {
"trovex": {
"args": [
"trovex"
],
"command": "uvx"
}
}
}
{
"mcpServers": {
"trovex": {
"args": [
"trovex"
],
"command": "uvx"
}
}
}
Persistent project memory for AI coding agents: one compact digest instead of re-reading the repo.
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Index your codebase. AI searches instead of re-reading files. 94% token savings.
Lossless skim-then-expand reading of big files, repos, and command output with far fewer tokens
Deterministic AGENTS.md and repo analysis for coding agents, paid per call in USDC over x402.
Persistent, read-only codebase memory for Claude Code; ask it instead of re-reading files.
MCP server that helps AI coding agents interact with AI code reviewers on GitHub PRs
Answers built from our own checks of this server.