io.github.vectros-ai/mcp-server

Official

by vectros-ai · TypeScript

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

Typed record store with hybrid search combining deterministic lookups and semantic memory.

Topics

aiai-agentsclaudeembeddingsgenaiknowledge-basellmmcpmodel-context-protocolragretrieval-augmented-generationsemantic-searchtypescriptvector-searchvectros

README.md18 KBView on GitHub

@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.

code

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:

bash

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.

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

Client	One-click	Manual
Claude Desktop	Desktop Extension (`.mcpb`) — 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	—	same JSON snippet as Claude Desktop
Smithery	`npx -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):

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)

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

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:

bash

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. Reopen the project in Claude Code and the Vectros tools are available.

Tools (21 tools)

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)

Tool	What it does
`list_schemas`	List the record-schema catalog the credential can see (filter by `surface` or resolve one by `recordType`). Makes `record_query` / `record_create` discoverable.
`record_query`	Query records by lookup field (equality / range / prefix, with `asc`/`desc` ordering) or list mode (filter by ownership + type).
`record_get`	Fetch one record by id, including its full payload (large payloads truncated to protect the agent context window).
`record_create`	Create a record of a given type; idempotent by `externalId`; optional per-record `indexMode`.
`record_update`	Patch a record's payload (deep-merged; `null` deletes a key); optimistic concurrency via `expectedVersion`.
`record_delete`	Permanently delete a record by id (leaves a tombstone).

Documents (text/file content, indexed for search + Q&A)

Tool	What it does
`document_ingest`	Create a document — inline text body OR local file upload (file mode is stdio-transport only). Idempotent by `externalId`; optional `schemaId` + `payload` for a typed, lookup-queryable document.
`document_query`	Query documents by lookup field (equality / range / prefix, with `asc`/`desc` ordering) or list mode (filter by ownership + type).
`document_get`	Fetch a document by id (metadata; optional text truncated at ~8K tokens; optional presigned `downloadUrl` for file-backed documents).
`document_update`	Patch a document's metadata / typed payload (deep-merged); optimistic concurrency via `expectedVersion`.
`document_delete`	Permanently delete a document by id (removes it and its indexed content).

Folders (group records + documents)

Tool	What it does
`folder_query`	Get a folder by id, or list folders (a parent's children for tree navigation, or a flat tenant list; paginated via `nextCursor`).
`folder_create`	Create a folder.
`folder_update`	Update a folder's name / description / ownership (merge-patch; optimistic concurrency via `expectedVersion`; folders cannot be re-parented).
`folder_delete`	Delete a folder.

Identity & history

Tool	What it does
`current_identity`	Describe the credential: tenantId, environment, principalType, and (where surfaced) allowedActions + dataScope.
`lookup_principal`	Resolve a user / org / client by your own `externalId` (→ its Vectros UUID, for the ownership filters) or by a schema lookup field. Read-only.
`version_history`	Read the audit/version trail (CREATE/UPDATE/DELETE, with actor + diff) for one record or document. Read-only.

All 21 tools wrap published Vectros HTTP API endpoints. JSON responses are what the agent sees as tool output. Per-call cost surfaces via the usage field on inference responses.

Opting into a subset

Pass VECTROS_MCP_TOOLS=hybrid_search,rag_ask to register only those two — useful for giving an agent read-only search access without exposing ingestion or inference costs to the credential. Unknown tool names fail fast at startup.

Resources

Two read-only resources for ambient context (no tool call required):

URI	What it returns
`vectros://schemas`	Same payload as `list_schemas`. Lets the agent preload schemas into context for ambient discovery.
`vectros://identity`	Same payload as `current_identity`. Lets the agent self-describe without spending a tool call.

Recommended credential

Use a scoped permanent API key (ssk_*), not a root key (sk_*).

A scoped key is bound to a narrowed AccessProfile — e.g. read-only across one orgId. If your MCP install is compromised, the blast radius is whatever the profile allows, not the whole tenant. The server emits a warn log line on startup when you pass a wildcard sk_* for exactly this reason.

The easiest way to get one is npx -y @vectros-ai/cli bootstrap (above) — it mints a least-privilege ssk_* and an AccessProfile for you, no root key required. To do it by hand instead: mint a scoped key from the developer portal under Keys → Create scoped key, bind it to an AccessProfile titled mcp-read-all or mcp-read-scoped, and drop the resulting ssk_live_... into the config above.

See the Vectros developer documentation on scoped tokens ("Recommended AccessProfile for MCP") for least-privilege credential setup — the vectros bootstrap flow provisions a scoped ssk_* key and its AccessProfile in one command.

Environment variables

Var	Required	Default	Purpose
`VECTROS_API_KEY`	yes	—	Vectros API key. Accepts `sk_` / `ssk_` / `st_`; `ssk_` recommended.
`VECTROS_API_BASE_URL`	no	`https://api.vectros.ai`	Override for staging or other envs. Validated: must be `https://` (or `http://` to localhost) and an official `*.vectros.ai` host.
`VECTROS_ALLOW_INSECURE_BASE_URL`	no	—	Set `1` to bypass the base-URL allow-list (e.g. a trusted local proxy). Not recommended — sends your key to an unvalidated host; logs a warning.
`VECTROS_MCP_INGEST_ROOT`	no	process cwd	Directory `document_ingest`'s `filePath` mode is jailed to. Paths escaping it (traversal/absolute/symlink) or matching a sensitive pattern are rejected.
`VECTROS_MCP_TOOLS`	no	(all tools)	Comma-separated tool names (e.g. `hybrid_search,rag_ask`).
`VECTROS_MCP_DEBUG`	no	—	Set `1` for verbose stderr logs.
`VECTROS_MCP_SKIP_PING_VALIDATION`	no	—	Set `1` to disable the startup `/v1/ping` check.
`VECTROS_MCP_HTTP_PORT`	HTTP only	`8765`	Port for HTTP transport.
`VECTROS_MCP_HTTP_HOST`	HTTP only	`127.0.0.1`	Bind address. Use `0.0.0.0` for all interfaces (then set a bearer token).
`VECTROS_MCP_HTTP_BEARER_TOKEN`	HTTP only	—	Client→server bearer token. Strongly recommended beyond localhost; required for a non-loopback bind.
`VECTROS_MCP_HTTP_ALLOWED_HOSTS`	HTTP only	—	Comma-separated extra `Host` values to allow (DNS-rebinding protection). Set to the public hostname(s) behind a reverse proxy.
`VECTROS_MCP_HTTP_ALLOWED_ORIGINS`	HTTP only	—	Comma-separated extra `Origin` values to allow.
`VECTROS_MCP_HTTP_ALLOW_INSECURE`	HTTP only	—	Set `1` to permit a non-loopback bind without a bearer token. Not recommended.

Startup credential validation

Before the first tool call, the server runs a GET /v1/ping check against your credential. Bad keys fail at startup with a clear error instead of opaquely 401'ing mid-conversation. Set VECTROS_MCP_SKIP_PING_VALIDATION=1 to disable.

HTTP transport

For hosted-MCP scenarios — running the server behind a network boundary, sharing it across multiple agent instances, deploying as a sidecar — the package also ships an HTTP binary:

bash

VECTROS_API_KEY=ssk_live_... \
VECTROS_MCP_HTTP_PORT=8765 \
VECTROS_MCP_HTTP_BEARER_TOKEN=$(openssl rand -hex 32) \
  npx -y -p @vectros-ai/mcp-server vectros-mcp-server-http

The HTTP binary is not the default — select it explicitly with npx -p <pkg> vectros-mcp-server-http. A bare npx -y @vectros-ai/mcp-server always starts the stdio server.

The server listens on http://127.0.0.1:8765/mcp by default. The bearer token is optional but strongly recommended for any deployment beyond localhost — without it, anyone who can reach the port can call Vectros with your credentials.

Health probe lives at GET /healthz (always unauthenticated, k8s readiness-friendly).

Current limitation: the server uses one upstream credential per process (the env VECTROS_API_KEY). Per-request credential override via the incoming Authorization header is a planned enhancement. For now, deploy one server per credential boundary you want.

Programmatic use (advanced)

Most consumers use the CLI shape above. If you need to embed the server in your own Node process:

import { VectrosMCPServer, createStdioTransport } from '@vectros-ai/mcp-server';

const server = new VectrosMCPServer({
  apiKey: process.env.VECTROS_API_KEY!,
  tools: ['hybrid_search', 'rag_ask'],
  resources: ['schemas'],     // opt-in resource filter; default = all
  validateOnStart: true,      // default — set false to skip startup ping
});
await server.connect(createStdioTransport());

What this server doesn't do (yet)

No prompts capability — /rag and /ingest_pdf slash-command templates land in a future release. (Provisioning — the bootstrap command — lives in the separate @vectros-ai/cli package, above.)
HTTP transport is single-tenant per process — per-request credential override via incoming Authorization header is a v1.0+ enhancement.
No Python implementation — TS only. Python users can npx this server from any project.
rag_ask and document_ask are not natively streaming — full answer aggregated before the tool returns. Progress notifications cover the latency. Native MCP-spec streaming lands when the spec stabilizes.

The server is on a pre-1.0 track toward a stable 1.0 release.

Rate limits

Tool calls hit the same per-account per-minute rate limit as any API client (writes, searches, and inference count against it; reads do not). On a 429 the server surfaces the error with its Retry-After hint so the agent can pace and retry rather than blind-retrying. See the rate limits guide for the per-plan limits.

Building from source

git clone https://github.com/vectros-ai/vectros-mcp-server
cd mcp-server
npm install
npm run build
npm test

npm install pulls @vectros-ai/sdk from the configured npm registry.

npm run build runs tsup to produce the dual ESM/CJS output in dist/. The SDK is bundled into the build (see tsup.config.ts) — the published npm package is self-contained and works without .npmrc config on the consumer's machine.

License

Apache-2.0. See the LICENSE file.

Install

Configuration

Environment variables

VECTROS_API_KEYrequiredsecret

Vectros API key. Use a least-privilege scoped key (ssk_...); mint one with `npx -y @vectros-ai/cli bootstrap`.

VECTROS_API_BASE_URL

Override the API base URL (default https://api.vectros.ai). Must be an official *.vectros.ai host.

VECTROS_MCP_TOOLS

Comma-separated subset of tool names to register (default: all 21 tools).

claude_desktop_config.json

{
  "mcpServers": {
    "mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@vectros-ai/mcp-server@0.6.0"
      ],
      "env": {
        "VECTROS_API_KEY": "<YOUR_VECTROS_API_KEY>",
        "VECTROS_API_BASE_URL": "<YOUR_VECTROS_API_BASE_URL>",
        "VECTROS_MCP_TOOLS": "<YOUR_VECTROS_MCP_TOOLS>"
      }
    }
  }
}