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Vectros AI MCP

by vectros-aiversion 0.18.2i★ –iRuns on your computer
Registry nameio.github.vectros-ai/mcp-server

Install

Overview

Description from the maker (original English)

Typed record store + hybrid search over MCP: deterministic lookups + semantic memory, per-customer.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory
  • You need to fill in: VECTROS_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: VECTROS_API_KEY, VECTROS_KEYRING_ALIAS, VECTROS_API_BASE_URL, VECTROS_MCP_TOOLS

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "vectros-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@vectros-ai/mcp-server"
      ],
      "env": {
        "VECTROS_API_KEY": "your-value",
        "VECTROS_KEYRING_ALIAS": "your-value",
        "VECTROS_API_BASE_URL": "your-value",
        "VECTROS_MCP_TOOLS": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add vectros-ai --env VECTROS_API_KEY=your-value --env VECTROS_KEYRING_ALIAS=your-value --env VECTROS_API_BASE_URL=your-value --env VECTROS_MCP_TOOLS=your-value -- npx -y @vectros-ai/mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "vectros-ai": {
      "command": "npx",
      "args": [
        "-y",
        "@vectros-ai/mcp-server"
      ],
      "env": {
        "VECTROS_API_KEY": "your-value",
        "VECTROS_KEYRING_ALIAS": "your-value",
        "VECTROS_API_BASE_URL": "your-value",
        "VECTROS_MCP_TOOLS": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "vectros-ai": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@vectros-ai/mcp-server"
      ],
      "env": {
        "VECTROS_API_KEY": "your-value",
        "VECTROS_KEYRING_ALIAS": "your-value",
        "VECTROS_API_BASE_URL": "your-value",
        "VECTROS_MCP_TOOLS": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • VECTROS_API_KEY secret required env
    Vectros API key. Use a least-privilege scoped key (ssk_...); mint one with `npx -y @vectros-ai/cli bootstrap`. Required for a registry install, which cannot assume any other tooling is present. If yo…
  • VECTROS_KEYRING_ALIAS env
    Optional. When VECTROS_API_KEY is unset and the vectros CLI is installed, resolve this keyring entry instead of the active one.
  • VECTROS_API_BASE_URL env
    Override the API base URL (default https://api.vectros.ai). Must be an official *.vectros.ai host.
  • VECTROS_MCP_TOOLS env
    Comma-separated subset of tool names to register (default: all 23 tools).

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account vectros-ai, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7282 servers in this category
  • Published to the registry more recently than 75% of the other 7282 servers in this category
  • Basic checks equal to or better than 100% of the other 7282 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Vectros AI MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: VECTROS_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.18.2) reached the registry 12 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

@vectros-ai/mcp-server

One-click badges install the server entry in your client. You still supply

a key — run npx -y @vectros-ai/cli bootstrap (recommended) or paste your

ssk_.... See Connect from your client and the

honest caveats.

A Model Context Protocol server for Vectros — a typed, multi-tenant record store unified with hybrid search and citation-grounded RAG. Deterministic lookups and enumeration *and* semantic search over one isolated, per-customer index of records and documents — so an agent gets memory that's precise, not just fuzzy recall. Reached agent-natively here over MCP (Claude Desktop, Cursor, Claude Code, Cline, Continue, VS Code, hosted platforms) — and the same data is human-accessible through the Vectros app + SDKs.

npx -y @vectros-ai/mcp-server

Your agent can search your indexed corpus, query structured records, ingest documents, and ask questions grounded against documents — reaching only your tenant's data, never the public web (there are no web tools).

Quick start — one command

The fastest way to set up is the @vectros-ai/cli bootstrap command. It mints a least-privilege scoped key (ssk_*) bound to a narrowed AccessProfile, optionally scaffolds a use-case data model, and safe-merges the vectros server into your MCP client config — no root key, and no hand-editing JSON:

npx -y @vectros-ai/cli bootstrap

You pick what to set up (a blank read-only credential, or a blueprint like task tracking) and sign in once with a token from the developer portal. The command then:

  • mints a scoped ssk_* for this machine (independently rotatable),
  • creates the matching AccessProfile — data-plane only; the command refuses to provision control-plane scope (keys / profiles / billing / …),
  • backs up and merges the entry into claude_desktop_config.json (Claude Desktop, Cursor, Cline). For Claude Code, add --client code: it merges the project .mcp.json and prints the equivalent claude mcp add command.

Restart your MCP client and you're done. It's idempotent (re-run any time); --rotate replaces this machine's key.

Want to browse the data yourself? bootstrap sets up the key for your *agent*, not a login for *you* — so a blueprint's context won't appear in the data-plane app's switcher until you join your own user to it (the app lists only contexts your user has access in). Grant yourself a role once, either in the admin app (Access → Contexts → _your context_ → Profiles → Create profile, pick yourself from the by-email picker, choose a role — no raw id needed) or from the CLI with --principal me (resolves to your own user):

vectros access grant --principal me --context <context-id> --role <role>

Blueprints that ship a human role (e.g. agentic-sdlc's editor) let you use --role; otherwise grant inline scopes with --actions records:r,search:r,….

For scripted / agent use, set the sign-in token in the environment and skip the prompts:

VECTROS_BOOTSTRAP_TOKEN=… npx -y @vectros-ai/cli bootstrap \

--blueprint task-management --yes

Prefer to wire it up by hand? See Configure manually below.

Connect from your client

ClientOne-clickManual
Claude DesktopDesktop Extension (.mcpb) — double-click, paste your keyJSON snippet
Cursor.cursor/mcp.json, same shape as below
VS Code.vscode/mcp.json, the same entry under a top-level servers key
Claude Codeclaude mcp add (below)project .mcp.json
Cline / Continue—same JSON snippet as Claude Desktop
Smitherynpx -y @smithery/cli install @vectros-ai/mcp-server—
Codex—TOML snippet (below)

The fastest path on every client is npx -y @vectros-ai/cli bootstrap — it mints a scoped key and writes the config for you. The one-click buttons install the server entry; you then supply the key (bootstrap, or paste your ssk_...).

Codex (~/.codex/config.toml):

[mcp_servers.vectros]

command = "npx" args = ["-y", "@vectros-ai/mcp-server"] env = { VECTROS_API_KEY = "ssk_live_..." }

Honest caveats

Precision is the pitch — what this server deliberately does *not* do:

  • There's a human step. Bootstrap needs a developer-portal sign-in / bridge token. One command, but a person signs in — there is no fully unattended provisioning.
  • No web tools, on purpose. The agent surface has no web-search or web-fetch tools at all. Vectros is the memory, not the browser.
  • Agent document upload is text-inline today. An agent ingests document text inline; on the stdio transport a jailed local-file upload is supported, but bulk file upload from the agent surface isn't the path today.
  • **Audit history is tamper-*evident*, not tamper-proof.** A state-continuity chain makes out-of-band alteration *detectable*; it is not continuous automated verification.

Configure manually (Claude Desktop or any MCP client)

If @vectros-ai/cli is installed globally (npm install -g @vectros-ai/cli, not a one-off npx run — the server needs vectros to still be on PATH later, when IT runs, not just while bootstrap ran) and you've run vectros bootstrap with it, omit the env block entirely — the server resolves your key from the local vectros CLI keyring automatically (see Credential resolution below). vectros login alone does not do this — it stores a separate sign-in session, not this key — so bootstrap is the step that actually has to have run:

{

"mcpServers": { "vectros": { "command": "npx", "args": ["-y", "@vectros-ai/mcp-server"] } } }

No CLI installed, or a machine you can't log in on? Set the key directly instead:

{

"mcpServers": { "vectros": { "command": "npx", "args": ["-y", "@vectros-ai/mcp-server"], "env": { "VECTROS_API_KEY": "ssk_live_..." } } } }

This file lives under Claude Desktop's own per-user app-support directory, not a shared repo — but a real key pasted into it is still a live secret at rest: don't paste it into a chat, ticket, or screenshot with the value intact, and prefer the keyring form above whenever you can.

Restart Claude Desktop. The agent now sees the Vectros tools and two resources as callable surfaces.

Configure manually (Claude Code)

Claude Code reads a project-scoped .mcp.json with the same shape. Never commit a real key into it — .mcp.json is exactly the kind of file teams check in to share a project's tooling, and a live ssk_*/sk_* value inside it is a credential leak the moment it lands in git history, not just if the repo is public.

The way to share this config safely: commit the entry with no `env` block at all, and let each developer's own vectros CLI keyring supply the key (see Credential resolution below — this is the same delegation git credential / docker-credential-* use). This needs each teammate to have @vectros-ai/cli installed globally and to have run vectros bootstrap with it — npx-ing the CLI once, or running vectros login alone (a separate sign-in session, not this key), leaves nothing on PATH or in the keyring for the server to find later:

{

"mcpServers": { "vectros": { "command": "npx", "args": ["-y", "@vectros-ai/mcp-server"] } } }

Or let Claude Code's CLI write that keyring-based entry for you:

claude mcp add vectros -- npx -y @vectros-ai/mcp-server

If a machine genuinely has no vectros CLI installed, you can pass the key directly with -e VECTROS_API_KEY=ssk_live_... — but then keep that config local: add .mcp.json to .gitignore first, rather than committing it with the key inside.

Add -e VECTROS_API_BASE_URL= to point the server at a different environment.

Load it into a session by restarting. A Claude Code session that was already open when you added the server won't pick it up mid-session — fully quit and reopen the project (not just re-select the tab). The /mcp panel shows the connector marketplace, not locally-configured stdio servers, so it won't confirm the server is loaded — ask the agent to call a Vectros tool instead. Config is keyed by the git common root, so a linked worktree resolves to its main repo's .mcp.json — add and open from the same project.

Windows note: if your .npmrc (or a global npm config) points the

@vectros-ai scope at another registry, a bare npx -y @vectros-ai/mcp-server

can resolve a build other than the public release. If the command above fails

to start, either remove the scoped-registry override for a plain npx run, or

pin an explicit version (npx -y @vectros-ai/mcp-server@ ) known to

work.

Tools (23 tools)

Search & RAG

ToolWhat it does
hybrid_searchHybrid BM25 + dense search across the tenant's indexed content (records + documents). Narrow by ownership (scope for one dimension, scopeFilters for several at once — e.g. one client within one org), folder, type, metadata filters, a created date window, and keyword-precision (textMode) / relevance floors. Returns the indexed projection of each hit.
rag_askAsk a question grounded against the indexed corpus. Scope retrieval through search (ownership — search.scope or multi-dimension search.scopeFilters — / folder / type / metadata filters / date window) and steer generation (instructions, and temperature for the models that accept one). Streaming generation aggregated; progress notifications keep the call alive for the generation window. Optional providerAlias routes one call through your own model provider (see below).
document_askAsk a question grounded against a single document. Same aggregation + progress-notification shape as rag_ask, including the optional providerAlias.

Records (structured, schema-validated data)

| Tool | What it d

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