Agenten stärken
Der eigene Befehl des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
npx -y @jonyoder/[email protected]Claude Code hat keinen Installationslink für einen lokalen Befehl. Kopieren Sie den Befehl unten und führen Sie ihn in einem Terminal aus.
claude mcp add jonyoder-code-index-mcp -- npx -y @jonyoder/code-index-mcp@0.1.10In einem Terminal ausführen.
{ "mcpServers": { "jonyoder-code-index-mcp": { "command": "npx", "args": [ "-y", "@jonyoder/[email protected]" ] } }}claude_desktop_config.json
Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden
Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
Semantic code search for AI coding assistants. Parses your source code, generates LLM-powered summaries, and builds a searchable vector index exposed via MCP.
code-index lets AI assistants like Claude Code search your codebase by instead of exact keywords:
MCP server for semantic code search using vector embeddings
"check if string is in slice" → finds StringInSlice in util.go
"how does authentication work" → finds auth packages, token handlers, middleware
"database transaction management" → finds BeginTransaction, CommitTransaction across packages
It works by generating natural language summaries of every function, file, and package, then embedding them as vectors for similarity search.
Download a pre-built binary from Releases, or install from source:
go install github.com/posit-dev/code-index/cmd/code-index@latest
Create .code-index.json in your repository root (see Configuration):
{
"project": "my-project",
"sources": [
{"path": "src", "language": "go"},
{"path": "frontend", "language": "typescript"}
],
"llm": {
"provider": "openai",
"base_url": "http://localhost:11434/v1",
"function_model": "llama3.2",
"summary_model": "llama3.2"
},
"embeddings": {
"provider": "openai",
"base_url": "http://localhost:11434/v1",
"model": "nomic-embed-text"
}
}
This example uses Ollama for fully local operation. You can also use OpenAI, AWS Bedrock, or any OpenAI-compatible API.
code-index all # parse → generate → build → embed
Add the MCP server to your project's .mcp.json:
{
"mcpServers": {
"code-index": {
"command": "npx",
"args": ["-y", "@jonyoder/code-index-mcp"]
}
}
}
Claude Code will use code_search proactively when working in your codebase.
If your team distributes the vector database via S3 or HTTP URL (see CI Setup), the MCP server automatically downloads it on first search — no manual setup needed.
parse → generate → build → embed → search
AST LLM docs JSON vectors query
MIT — see LICENSE.
MCP server for semantic code search using vector embeddings
{ "mcpServers": { "jonyoder-code-index-mcp": { "command": "npx", "args": [ "-y", "@jonyoder/[email protected]" ] } }}Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
@jonyoder/code Index läuft lokal über stdio.
Zuletzt geprüft: 2026-10-28. MCP server for semantic code search using vector embeddings