Typed record store + hybrid search over MCP: deterministic lookups + semantic memory, per-customer.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
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 yourssk_.... 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.
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).
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:
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:
ssk_* for this machine (independently rotatable),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):
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,β¦.
For scripted / agent use, set the sign-in token in the environment and skip the prompts:
Prefer to wire it up by hand? See Configure manually below.
| Client | One-click | Manual |
|---|---|---|
| Claude Desktop | Desktop Extension (.mcpb) β double-click, paste your key | JSON snippet |
| Cursor | .cursor/mcp.json, same shape as below | |
| VS Code | .vscode/mcp.json, same shape | |
| Claude Code | claude mcp add (below) | project .mcp.json |
| Cline / Continue | β | same JSON snippet as Claude Desktop |
| Smithery | npx -y @smithery/cli install @vectros-ai/mcp-server | β |
| Codex | β | 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):
Precision is the pitch β what this server deliberately does not do:
Restart Claude Desktop. The agent now sees the Vectros tools and two resources as callable surfaces.
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):
Or let Claude Code's CLI write it for you:
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-aiscope at a private registry, a barenpx -y @vectros-ai/mcp-servercan 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 plainnpxrun, or pin an explicit version (npx -y @vectros-ai/mcp-server@<version>) known to work.
Search & RAG
| Tool | What it does |
|---|---|
hybrid_search | Hybrid 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_ask | Ask 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_ask | Ask a question grounded against a single document. Same aggregation + progress-notification shape as rag_ask. |
Records (structured, schema-validated data)
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