valv
Let agents query your database. Just not all of it.
valv gives an agent structured tools to read your database — and, opt-in, to write to it. The model emits a structured query (or insert/update/delete) — never a native database command — and valv validates it against your schema, scopes it to the current user with policies you write in code, compiles it for your database, and runs it.
The model's query is treated as fully untrusted. It can't read a column you hid, a row the user isn't allowed to see, call a function you didn't allow, write a column you didn't permit, or escape its tenant on a write. Valv rebuilds and checks the query on the server before it reaches the database adapter.
const valv = await createValv(client, { schema: "introspect", defaultPolicy: "deny-all" })
valv.policy("orders", (ctx) => ({
read: { tenant_id: ctx.tenant.id }, // every read is scoped to this tenant
fields: { deny: ["internal_notes"] }, // this column never reaches the model
}))
const tools = await valv.tools.aisdk(ctx) // hand to your agent — it queries safely
Two ways to use it
- In your app. Configure valv in code, write policies against your request context, and hand the tools to your agent — Vercel AI SDK, Anthropic, OpenAI, or Gemini. Or expose those same tools over MCP with
@valv/mcp-sdk, scoped per request. - With a coding agent. Point
@valv/mcpat a database and a tool like Claude Code queries it safely — no code required.
Quick start
Install an adapter for your database (it pulls in @valv/core):
npm i @valv/clickhouse @clickhouse/client # ClickHouse
# or
npm i @valv/prisma @prisma/client # Postgres / MySQL / SQLite
# or
npm i @valv/mongodb mongodb # MongoDB
Wire it up — connect, write a policy, hand the tools to an agent:
import { createValv } from "@valv/clickhouse"
import { generateText, stepCountIs } from "ai"
// 1. Connect — introspect the live schema (or pass a hand-defined one).
const valv = await createValv(client, { schema: "introspect", defaultPolicy: "deny-all" })
// 2. Policy — what this caller may read, resolved from your context.
valv.policy("orders", (ctx) => ({ read: { tenant_id: ctx.tenant.id } }))
// 3. Tools — bound to the request's context, formatted for your provider.
const ctx = { user: { id: "u1", role: "analyst" }, tenant: { id: "acme" } }
const { text } = await generateText({
model,
system: await valv.instructions(ctx), // how to drive the tools + the caller's resources
tools: await valv.tools.aisdk(ctx),
stopWhen: stepCountIs(6),
prompt: "What's our revenue per order status this month?",
})
The agent gets four tools — list_resources, search_resources, describe_resource, and query — discovers your schema, and runs a query. valv scopes it to acme, compiles it to ClickHouse SQL, runs it, and hands back rows.
What the agent can express
One query tool covers the whole read surface. The grammar is Prisma-idiomatic — a shape models already know cold — and desugars server-side into a checked query:
{
"from": "orders",
"select": {
"status": true, // a plain column
"orders": { "count": true }, // count(*) — the key names the output
"revenue": { "sum": "total" } // an aggregate
},
"where": { "created_at": { "gte": "2026-06-01" } },
"groupBy": ["status"],
"orderBy": { "revenue": "desc" },
"take": 10
}
That's enough for real analytics — filters ({ field: value } equality, operator objects like { gte, lt, in, contains }, and AND/OR/NOT trees), aggregates, time-series (bucket with a function and group by the alias), top-N (order by an aggregate), and conditional aggregation (countIf, sumIf). ClickHouse adds dialect functions like quantileTiming and toStartOfInterval; every function is type-checked and its literals parameterized.
Joins
To read a related resource, reference its column with a dotted path from the root. The model can only follow relations declared in your schema; valv derives the joins, picks the keys, and composes the policy of every table it touches — each joined table is scoped by its own policy and field allowlist, so a join can never reach a hidden column or another tenant's rows.
{
"from": "orders",
"select": {
"customer_name": { "col": "customer.name" }, // one hop: orders → customer
"region": { "col": "customer.region.name" }, // multi-hop: → customer → region
"revenue": { "sum": "total" }
},
"groupBy": ["customer.name"]
}
belongsTo and hasMany relations are supported; join depth, table count, and fan-out are capped, and every query runs under a statement timeout. Relations are auto-introspected on Prisma and declared in the schema on ClickHouse.
Usage
Connect
createValv is async — it loads the schema on construction, so the instance is ready to use. Call it once at startup.
// ClickHouse — introspect, or hand-define a schema
const valv = await createValv(clickhouseClient, { schema: "introspect", database: "analytics" })
// Prisma (Postgres / MySQL / SQLite / Cockroach) — schema comes from your .prisma file
import { createValv } from "@valv/prisma"
const valv = await createValv(prismaClient)
// MongoDB — merge collection validators with sampled document fields
import { createValv } from "@valv/mongodb"
const valv = await createValv(mongoClient.db("analytics"), { schema: "introspect" })
defaultPolicy: "deny-all" (recommended) makes a resource invisible until you write a policy for it.
Policy
A policy is a function of your context. It decides what the caller may read, per resource:
valv.policy("orders", (ctx) => ({
read: { tenant_id: ctx.tenant.id }, // row filter — AND-injected into every query
fields: { deny: ["internal_notes"] }, // hide columns
}))
valv.policy("users", (ctx) => ({
read: { tenant_id: ctx.tenant.id },
fields: ctx.user.role === "support" ? { deny: ["email"] } : undefined,
}))
The model can't widen or override the row filter. Valv injects it after parsing
the model's query and before handing the query to the database adapter. Fields
are denied two ways: fields.deny (a blacklist) or fields.allow (a
whitelist). Denied and unknown columns fail with the same message, so the model
can't probe for hidden columns. Use "*" as the resource name for a default
policy.
The same policy object carries the write axes — create, update, delete (and write as a shorthand for create+update) — which default to denied. See Writes.
Tools
valv.tools.<format>(ctx, options) returns provider-ready tools, bound to that context. Discovery is policy-filtered — list/search/describe only surface what the caller may read.
valv.tools.anthropic(ctx) // Anthropic Messages API
valv.tools.openai(ctx) // OpenAI / compatible
valv.tools.gemini(ctx) // Google Gemini
await valv.tools.aisdk(ctx) // Vercel AI SDK (async; needs `ai`)
valv.tools.neutral(ctx) // raw, framework-agnostic
valv.tools.anthropic(ctx, { list: false, search: false }) // drop discovery tools individually
The aisdk format returns self-executing tools (the SDK runs them). The provider formats (anthropic/openai/gemini) return tool definitions for the API request; you dispatch a tool call with runTool:
const result = await valv.runTool(call.name, call.input, ctx)
The discovery tools (list/search/describe) are on by default; the write tools (create/update/delete) are off by default — turn them on per call:
valv.tools.aisdk(ctx, { search: false, create: true, update: true })
System prompt
await valv.instructions(ctx) returns a drop-in system-prompt block: how to drive the tools (discover → describe → query, filters are scoped server-side) plus the resources this caller may read — so the model can skip the opening list_resources round-trip. Put it in your system prompt alongside the tools. The static text is also exported as AGENT_INSTRUCTIONS if you'd rather compose the resource list yourself.
const system = await valv.instructions(ctx)
You answer questions by querying a set of resources through the provided tools. Access is
enforced server-side: every query is scoped to what the current caller may read, so you never
need to add tenant/owner/permission filters yourself — a query returns only permitted rows.
Workflow:
1. Find the resource: use list_resources / search_resources; you often already have the list below.
2. Before querying an unfamiliar resource, call describe_resource to get its exact column names,
types, and relations. Don't guess column names.
3. Query with the `query` tool. Do the work in the query — filter with `where`, aggregate with
functions, `groupBy`, `orderBy`, `take` — rather than pulling raw rows and reducing yourself.
4. The grammar is Prisma-like. `select` is an object keyed by output name: `true` for a plain
column, { "col": "path" } to rename or reach a joined column, { fn: args } to aggregate (e.g.
{ "revenue": { "sum": "amount" } }). `where` uses { field: value } for equality and
{ field: { gte, lt, in, contains } } for operators, combined with AND/OR/NOT.
5. Read a joined resource's column with a dotted path from the root — "customer.name" — in a
select `col` or a where key. A root column takes no dot; only declared relations join.
If a call is rejected, read the error and fix the query — don't retry the same shape.
Resources you can query:
- orders — customer orders
- customers — people who place orders
Writes
Writes are off until you both allow them in policy and expose the tool. Each is its own tool and its own policy axis, with stronger guarantees than reads — the model can't set columns you didn't permit, can't aim a row at another tenant, and can't run an unscoped update/delete:
valv.policy("orders", (ctx) => ({
read: { tenant_id: ctx.tenant.id },
create: { tenant_id: ctx.tenant.id }, // tenant_id is force-set on insert
update: { tenant_id: ctx.tenant.id }, // AND-injected into the WHERE
delete: false, // never deletable
fields: { readOnly: ["status"] }, // readable, not writable
}))
await valv.create({ from: "orders", data: { status: "pending", total: 1200 } }, ctx)
await valv.update({ from: "orders", data: { status: "shipped" }, where: { /* …Prisma filter */ } }, ctx)
createforce-injects the policy's owned fields (tenant_id) onto the row — the model can't choose, omit, or override them.update/deleteAND the policy predicate into yourwhere, which is required (no implicit "all rows"). The model can only touch rows within its scope.- The columns a write sets are checked against a writable allowlist (separate from readable); scope columns, sensitive fields, and
readOnlyfields aren't writable. Awherecan only filter by columns the caller can read. - Databases: Prisma supports all three operations for PostgreSQL, MySQL,
SQLite, and CockroachDB. ClickHouse supports
createonly. MongoDB is read-only.
Saved queries & dashboards
Because the model emits a plain query object, you can store it and re-run it — a dashboard that refreshes without the LLM in the loop. Replays go through the full pipeline every time, so policy is always re-applied for the current viewer:
await db.saveWidget(id, { query }) // it's just JSON — persist it anywhere
const rows = await valv.run(widget.query, ctx) // fresh data, re-scoped to ctx
const columns = valv.resultSchema(widget.query) // output columns + types, without running it
resultSchema derives the output shape ([{ name, type }]) from the query alone — handy for driving chart config and detecting drift when the schema changes. A stored query is never trusted: it's re-validated on every replay, so it can't outlive the permissions it was created under.
How it works
LLM ──emits──▶ query (structured JSON, untrusted)
│
▼
validate check every column/function against the catalog + policy
│
▼
inject AND the tenant/row filter into WHERE
│
▼
compile produce SQL or a native database query
│
▼
execute ──▶ your database ──▶ serialized rows
A worked example. The agent asks for revenue per status and emits:
{ "from": "orders",
"select": { "status": true, "revenue": { "sum": "total" } },
"groupBy": ["status"] }
With the policy read: { tenant_id: ctx.tenant.id } and ctx.tenant.id = "acme", valv emits:
SELECT `status`, sum(`total`) AS `revenue`
FROM `orders`
WHERE (`tenant_id` = {p0:String}) -- ← injected; the model never wrote this
GROUP BY `status`
-- params: p0 = "acme"
The model never wrote the WHERE clause, and it can't remove it. If it had
selected a denied column (internal_notes), referenced an unknown function, or
hidden a sensitive column inside a sumIf predicate, validation would have
rejected the query before the adapter compiled it. SQL adapters bind values as
parameters instead of concatenating strings. MongoDB emits typed aggregation
pipeline values. Safety doesn't depend on the model behaving.
Connect a coding agent (MCP)
Expose your database to an agent like Claude Code over the Model Context Protocol — same tools, same policy enforcement.
Zero-config server
@valv/mcp needs no code. Run the guided setup, which probes your database and writes the config for you:
npx @valv/mcp init
Or wire it by hand in your .mcp.json — point it at a connection string:
{
"mcpServers": {
"db": {
"command": "npx",
"args": ["-y", "@valv/mcp"],
"env": { "DATABASE_URL": "postgresql://user:pass@localhost:5432/app" }
}
}
}
It introspects the live schema, serves the four tools read-only by default, and works with Prisma-supported SQL databases, ClickHouse, and MongoDB. Narrow access with VALV_TABLES / VALV_EXCLUDE, or take full control with a VALV_POLICY_FILE.
In your app
@valv/mcp-sdk turns a valv instance you configure into an MCP server, with policy and per-request context in your hands:
import { startStdioServer } from "@valv/mcp-sdk"
const valv = await createValv(client, { schema: "introspect", defaultPolicy: "deny-all" })
valv.policy("orders", (ctx) => ({ read: { tenant_id: ctx.tenant.id } }))
await startStdioServer(valv, {
context: () => resolveIdentity(), // resolved per request (env, headers, …)
})
Charting skill
skills/valv is a Claude Code skill that turns a data question into a chart: it queries through the valv MCP and renders the result as a self-contained Chart.js HTML file. Ask it to "visualize revenue by month" and it discovers the schema, runs one structured query, and opens the chart.
It also learns your database as you use it. The first time it describes a table, figures out the dialect's time-bucket function, or maps "revenue" to sum(total) on orders, it records that in .valv/notes.md in your working directory — so later sessions skip the rediscovery and start warm. The notes hold schema and semantics only, never result rows, and the file is plain markdown you can read, edit, or pre-seed yourself.
Adapters
Everything above the adapter (the query grammar, validation, policy injection, and the tool layer) lives in @valv/core and is database-agnostic. Each adapter introspects its database and runs the validated, policy-injected query. SQL adapters share one emitter; the MongoDB adapter compiles the same query into an aggregation pipeline.
Examples
examples/hand-schema— offline, no database: a hand-defined schema, queries, andresultSchema. The fastest way to see the pipeline.examples/mongodb— MongoDB introspection, tenant policy, field allowlisting, and a grouped aggregation.examples/clickhouse-analytics— an agent answering analytics questions over ClickHouse.examples/ecommerce— an agent over Postgres (Prisma), plus a saved-query dashboard.
License
MIT