Vectora runs on your own machine — the client starts it, so there is no endpoint to ping. 70 installs a week from pypi. Last commit 29 Oct 2026.
AI assistant with RAG, web search, filesystem and memory. MCP sub-agent for Claude Code.
Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 24 September 2026. No other catalogue keeps this.
We read the source, 4 d ago · rules 995398eba69f
Things with no honest explanation: a promise that contradicts the code, code that runs at install time while hiding what it does, data leaving the machine.
"com confiança (ex.: rm -rf, git push --force, apagar recursos)."
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
result = subprocess.run( # nosec B603, B607
re.compile(r"(id_rsa|id_dsa|id_ecdsa|id_ed25519)(\.pub)?$"),
for child in sorted(m.iterdir(), key=lambda p: p.name.lower())
if _should_install_terminal_signals(dict(os.environ), force_web=force_web):
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
Code changes quietly between releases, and nobody reads the diff of a dependency. We do, on every release — watch Vectora and you get told the day something new turns up.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add vectora -e 'COHERE_API_KEY=<COHERE_API_KEY>' -e 'TAVILY_API_KEY=<TAVILY_API_KEY>' -- uvx vectora-agent
{
"mcpServers": {
"vectora": {
"command": "uvx",
"args": [
"vectora-agent"
],
"env": {
"COHERE_API_KEY": "<COHERE_API_KEY>",
"TAVILY_API_KEY": "<TAVILY_API_KEY>"
}
}
}
}
[mcp_servers.vectora]
command = "uvx"
args = ["vectora-agent"]
env = { COHERE_API_KEY = "<COHERE_API_KEY>", TAVILY_API_KEY = "<TAVILY_API_KEY>" }
{
"mcpServers": {
"vectora": {
"command": "uvx",
"args": [
"vectora-agent"
],
"env": {
"COHERE_API_KEY": "<COHERE_API_KEY>",
"TAVILY_API_KEY": "<TAVILY_API_KEY>"
}
}
}
}
{
"servers": {
"vectora": {
"type": "stdio",
"command": "uvx",
"args": [
"vectora-agent"
],
"env": {
"COHERE_API_KEY": "<COHERE_API_KEY>",
"TAVILY_API_KEY": "<TAVILY_API_KEY>"
}
}
}
}
Put your own values in place of
<COHERE_API_KEY> (Cohere API key — required for RAG embeddings and reranking), <TAVILY_API_KEY> (Tavily API key — required for web search and URL extraction).
The author’s registry entry marks them as required.
Optional settings from the registry entry, left out of the block above:
GOOGLE_API_KEY (Google Gemini API key (recommended free-tier LLM provider)), OPENAI_API_KEY (OpenAI API key (optional — alternative LLM provider)), ANTHROPIC_API_KEY (Anthropic API key (optional — alternative LLM provider)), LANGSMITH_API_KEY (LangSmith API key (optional — tracing and observability)).
Web search and clean-text page fetch for AI agents, with SSRF protection.
Free web search and long-term memory for AI assistants. No API keys; data stays local.
Rust MCP server for denoised web search — fetch, clean, and rerank web content for AI agents.
Rust MCP server for denoised web search — fetch, clean, and rerank web content for AI agents.
Rust MCP server for denoised web search — fetch, clean, and rerank web content for AI agents.
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Metalift MCP for AI agents: web search (2 credits), scrape, crawl, and map tools
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Answers built from our own checks of this server.