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  3. Vectros MCP Server
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Vectros MCP Server

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mcp-server-166": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-server-166"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

@vectros-ai/mcp-server

npm license

Add to Cursor Install in VS Code Claude Desktop Extension

One-click badges install the server entry in your client. You still supply a key β€” 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 β€” 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 β€” 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) β€” and the same data is human-accessible through the Vectros app + SDKs.

Terminal
npx -y @vectros-ai/mcp-server

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

Quick start β€” 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 β€” no root key, and no hand-editing JSON:

Terminal
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 β€” 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 β€” 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 β†’ Contexts β†’ your context β†’ Profiles β†’ Create profile, pick yourself from the by-email picker, choose a role β€” no raw id needed) or from the CLI with --principal me (resolves to your own user):

bash
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:

bash
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) β€” double-click, paste your keyJSON snippet
CursorAdd to Cursor.cursor/mcp.json, same shape as below
VS CodeInstall in VS Code.vscode/mcp.json, same shape
Claude Codeclaude mcp add (below)project .mcp.json
Cline / Continueβ€”same JSON snippet as Claude Desktop
Smitherynpx -y @smithery/cli install @vectros-ai/mcp-serverβ€”
Codexβ€”TOML snippet (below)

The fastest path on every client is npx -y @vectros-ai/cli bootstrap β€” 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):

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

Honest caveats

Precision is the pitch β€” 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 β€” 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)

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

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 β€” drop this at your project root (commit it to share the server with the repo):

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

Or let Claude Code's CLI write it for you:

Terminal
claude mcp add vectros -e VECTROS_API_KEY=ssk_live_... -- npx -y @vectros-ai/mcp-server

Add -e VECTROS_API_BASE_URL=https://api.staging.vectros.ai for a non-production 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 β€” 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 β€” 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 β€” add and open from the same project.

Windows note: if your .npmrc (or a global npm config) points the @vectros-ai scope at a private registry, a bare npx -y @vectros-ai/mcp-server can resolve an unexpected internal build there instead of the public release β€” and an internal build is not guaranteed to run on Windows. 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@<version>) known to work.

Tools (22 tools)

Search & RAG

ToolWhat it does
hybrid_searchHybrid BM25 + dense search across the tenant's indexed content (records + documents). Narrow by ownership, 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 (ownership / folder / type / metadata filters / date window) and steer generation (instructions / temperature). Streaming generation aggregated; progress notifications keep the call alive for the generation window.
document_askAsk a question grounded against a single document. Same aggregation + progress-notification shape as rag_ask.

Records (structured, schema-validated data)

Read the full README β†’View source on GitHub β†’

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Frequently Asked Questions about Vectros MCP Server

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "vectros-mcp-server": { "command": "npx", "args": ["-y", "vectros-ai/vectros-mcp-server"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
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28Quality signal: Emerging Β· 28/100How this signal is calculated β–Ύ
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools12/30
Adoption & activity1/15
Community engagement0/10

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