Agenten stärken
Der eigene Befehl des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
uvx embgrep==0.1.0Claude Code hat keinen Installationslink für einen lokalen Befehl. Kopieren Sie den Befehl unten und führen Sie ihn in einem Terminal aus.
claude mcp add io.github.arknill-embgrep -- uvx embgrep==0.1.0In einem Terminal ausführen.
{ "mcpServers": { "io.github.arknill-embgrep": { "command": "uvx", "args": [ "embgrep==0.1.0" ] } }}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.
StartenSearch your codebase and documentation by meaning, not just keywords. embgrep indexes files into local embeddings and lets you run semantic queries — no API keys, no cloud services, no vector database servers.
.py, .js, .ts, .java, .go, .rs, .md, .txt, .yaml, .json, .toml, and morepip install embgrep # core (fastembed + numpy)
pip install embgrep[cli] # + click/rich CLI
pip install embgrep[mcp] # + FastMCP server
pip install embgrep[all] # everything
from embgrep import EmbGrep
eg = EmbGrep()
# Index a directory
eg.index("./my-project", patterns=["*.py", "*.md"])
# Semantic search
results = eg.search("database connection pooling", top_k=5)
for r in results:
print(f"{r.file_path}:{r.line_start}-{r.line_end} (score: {r.score:.4f})")
print(f" {r.chunk_text[:80]}...")
# Incremental update (only changed files)
eg.update()
# Index statistics
status = eg.status()
print(f"{status.total_files} files, {status.total_chunks} chunks, {status.index_size_mb} MB")
eg.close()
# Index a project
embgrep index ./my-project --patterns "*.py,*.md"
# Search
embgrep search "error handling patterns"
# Filter by file type
embgrep search "async database query" --path-filter "%.py"
# Check status
embgrep status
# Update changed files
embgrep update
import embgrep
embgrep.index("./src")
results = embgrep.search("authentication middleware")
status = embgrep.status()
embgrep.update()
Add to your Claude Desktop / MCP client configuration:
{
"mcpServers": {
"embgrep": {
"command": "embgrep-mcp"
}
}
}
Or with uvx:
{
"mcpServers": {
"embgrep": {
"command": "uvx",
"args": ["--from", "embgrep[mcp]", "embgrep-mcp"]
}
}
}
flowchart TD
A["📁 Files"] --> B["Smart Chunking\ncode: function-level\ndocs: heading-level"]
B --> C["fastembed\nlocal embeddings"]
C --> D["SQLite\nvector index"]
D --> E["🔍 Query"]
E --> F["Cosine Similarity\nranked results"]
F --> G["✅ Matches\nwith context"]
Chunking — Files are split into semantically meaningful chunks:
.py, .js, .ts, etc.): split by function/class boundaries.md, .txt): split by headings or paragraph breaksEmbedding — Each chunk is converted to a 384-dimensional vector using BGE-small-en-v1.5 via ONNX Runtime (no PyTorch needed)
Storage — Embeddings are stored as BLOBs in a local SQLite database
Search — Query text is embedded and compared against all chunks using cosine similarity
MIT
Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.
Local semantic search — embedding-powered grep for files, zero external services.
{ "mcpServers": { "io.github.arknill-embgrep": { "command": "uvx", "args": [ "embgrep==0.1.0" ] } }}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.
Embgrep läuft lokal über stdio.
Zuletzt geprüft: 2026-11-08. Local semantic search — embedding-powered grep for files, zero external services.