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8 days ago
glama

VecGrep

Semantic code search MCP server that reduces token usage by ~95% by returning top relevant code chunks instead of full files.

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// key findings
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
🔐
You'll be asked for 3 credentials: VECGREP_OPENAI_KEY, VECGREP_VOYAGE_KEY, VECGREP_GEMINI_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// required environment variables
This server reads these from process.env. You'll be asked to provide them before it can run.
configVECGREP_MODELisuruwijesiri/all-MiniLM-L6-v2-code-search-512 HuggingFace model ID (local provider only)
configVECGREP_BACKENDonnx Local backend: onnx (fastembed, fast startup) or torch (sentence-transformers, any HF model)
🔐 secretVECGREP_OPENAI_KEYopenai text-embedding-3-small 1536 vecgrep[openai]
🔐 secretVECGREP_VOYAGE_KEYvoyage voyage-code-3 1024 vecgrep[voyage]
🔐 secretVECGREP_GEMINI_KEYgemini gemini-embedding-exp-03-07 3072 vecgrep[gemini]
// full audit trail
The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.
// improvement guidance — verified publishers only
We have 4 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/vecgrep-vecgrep-1aivow)](https://m8ven.ai/mcp/vecgrep-vecgrep-1aivow)
commit: 6c54124c399c76754513be037423c75d7835a740
code hash: 9990b60d365d7e221227a352a8df853e80a9da4ccd470cdcf4abbc3fc0889b9c
verified: 6/22/2026, 12:04:18 PM
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