Rpg Encoder runs on your own machine — the client starts it, so there is no endpoint to ping. 105 installs a week from npm. Last commit 14 Apr 2026.
Semantic code graph for AI-assisted code understanding via tree-sitter and MCP.
We read the source, 19 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.
postinstall: node install.js
этот файл ставится пользователю, но в репозитории его нет
execFileSync(bin, process.argv.slice(2), { stdio: "inherit" });
const tmpFile = path.join(BIN_DIR, archive);
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 rpg-encoder -- npx -y rpg-encoder
{
"mcpServers": {
"rpg-encoder": {
"args": [
"-y",
"rpg-encoder"
],
"command": "npx"
}
}
}
[mcp_servers.rpg-encoder]
command = "npx"
args = ["-y", "rpg-encoder"]
{
"mcpServers": {
"rpg-encoder": {
"args": [
"-y",
"rpg-encoder"
],
"command": "npx"
}
}
}
{
"mcpServers": {
"rpg-encoder": {
"args": [
"-y",
"rpg-encoder"
],
"command": "npx"
}
}
}
Semantic code graph MCP server for coding agents
Codebase understanding for AI coding agents — symbol graph, blast radius, semantic search.
MCP server for code structure analysis using tree-sitter.
Graph-powered code intelligence with semantic search and knowledge graph for AI assistants
MCP server for code structure analysis using tree-sitter.
Repository knowledge graph MCP server for codebase understanding and debugging.
Evidence-traced codebase understanding and security scanning for AI agents over MCP.
Persistent understanding via workflow prompts, typed updates, graph-native code, and supersession.
Answers built from our own checks of this server.