Cowork Semantic SearchSAFE
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
[](https://github.com/ZhuBit/cowork-semantic-search/stargazers) [](https://www.python.org/downloads/) [](LICENSE) [](https://modelcontextprotocol.io)
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Local semantic search for your documents. No API keys. No cloud. Works with any MCP client.
Why
AI coding tools are powerful, but they have blind spots when it comes to your local files:
- Frozen knowledge -- training data has a cutoff. Your latest reports, notes, and contracts don't exist in the model's world.
- Context window limits -- you can't paste 500 documents into a prompt.
- No cross-file search -- your AI tool can read one file at a time, but can't search across your entire document library for the relevant pieces.
This plugin bridges that gap. It indexes your local documents into a small, fast vector database. When you ask a question, it retrieves only the relevant pieces -- so your AI tool can answer with your actual data.
Your documents --> chunked --> embedded --> local vector DB | Your question --> embedded --> similarity search --> relevant chunks --> AI answers
Features
- Fully offline -- one-time model download (~120MB), then no network calls. No data leaves your machine.
- Incremental indexing -- SHA-256 content hashing. Only changed files get reprocessed. Re-indexing 1000 files where 3 changed takes seconds.
- Multilingual -- handles 50+ languages natively. Search in one language, find results in another.
- Hybrid search -- combines semantic simi
7296583594eaOBSERVED · 2026-10-08Connect
Built from this server's own package name, version and transport as found in its source — not copied from anyone's documentation, so it cannot drift against a page we do not control.
claude mcp add cowork-semantic-search -- uvx cowork-semantic-search
{
"mcpServers": {
"cowork-semantic-search": {
"command": "uvx",
"args": [
"cowork-semantic-search"
]
}
}
}Exposed tools (1)
1 read · 0 write · 0 destructive.
| Tool | Risk | Description |
|---|---|---|
get_index_status | read | Get status information about the current search index. |
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- not all pinned
- Secrets in source
- none-found
Findings (1)
fastmcp, sentence-transformers, lancedb, langchain-text-splitters, pytest
Gates applied: no_behavioural_pass.
7296583594eafull audit observations/trust-audit/mcp-server/zhubit__cowork-semantic-search.json · Report an issue / request a re-scanAudit history
Every audit this server has had. A grade with a past is a grade somebody is still checking.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | 7296583594ea | SAFE | B | 89 | first audit |
Questions
What tools does Cowork Semantic Search expose?
1 in total: 1 read-only, 0 that write, and 0 that can delete or overwrite. Every one is listed on this page with its risk.
Is Cowork Semantic Search safe to connect to an agent?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean server reads B.
What credentials does Cowork Semantic Search need?
No credential environment variables were found in its source, so it appears to need none.
How current is this page?
The grade is for one exact copy of the source (7296583594ea), read on 2026-10-08. The repository is watched and re-audited when it changes.