Name: catlog22/codexlens-search
Rating: 3.2 (1 reviews)
Author: catlog22

catlog22/codexlens-search

Score: 54.4 Rank #30

Lightweight semantic code search engine — 2-stage vector + FTS + RRF fusion + MCP server for Claude Code

Overview

catlog22/codexlens-search is a Python MCP server licensed under MIT. Lightweight semantic code search engine — 2-stage vector + FTS + RRF fusion + MCP server for Claude Code

Ranked #30 out of 104 indexed tools.

Actively maintained with commits in the last week.

Ecosystem

Python MIT

Score Breakdown

Stars 15% 2

2 stars → early stage

Freshness 25% today

Last commit today → actively maintained

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 1

1 contributor → solo project

Dependents 25% 0

No dependents → no downstream usage

Forks 0

Description Good

License MIT

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

How to Improve

Description low impact

Expand your description to 150+ characters for better discoverability

Contributors medium impact

Single-contributor projects carry bus-factor risk — welcoming contributors boosts confidence

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

Badge

Markdown

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Embed Widget docs →

HTML

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Details

Stars: 2
Forks: 0
Open Issues: 0
Closed Issues: 0
Contributors: 1
Dependents: —
Language: Python
License: MIT
Last Commit: today

Matched Queries

"mcp server""mcp-server"

From the README

# codexlens-search

Semantic code search engine with MCP server for Claude Code.

2-stage vector search + FTS + RRF fusion + reranking — install once, configure API keys, ready to use.

## Quick Start (Claude Code MCP)

Add to your project `.mcp.json`:

```json
{
  "mcpServers": {
    "codexlens": {
      "command": "uvx",
      "args": ["--from", "codexlens-search[mcp]", "codexlens-mcp"],
      "env": {
        "CODEXLENS_EMBED_API_URL": "https://api.openai.com/v1",
        "CODEXLENS_EMBED_API_KEY": "${OPENAI_API_KEY}",
        "CODEXLENS_EMBED_API_MODEL": "text-embedding-3-small",
        "CODEXLENS_EMBED_DIM": "1536"
      }
    }
  }
}
```

That's it. Claude Code will auto-discover the tools: `index_project` → `search_code`.

## Install

```bash
# Standard install (includes vector search + API clients)
pip install codexlens-search

# With MCP server for Claude Code
pip install codexlens-search[mcp]
```

Optional extras for advanced use:

| Extra | Description |
|-------|----------

Read full README on GitHub →