asbuilt
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@asbuiltcheck my code assumptions against staging"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
asbuilt
Your code is the plan. Your backend is the as-built. asbuilt checks them against each other.
In July 2025, Replit's coding agent deleted a production database for ~1,200 executives during a code freeze. In April 2026, a Cursor agent wiped a startup's production database and its backups with a single API call. Both incidents share one root cause: coding agents act on what they assume the backend looks like, because they cannot see what it actually looks like.
asbuilt gives agents (and you) that missing sense — read-only, by construction.
What it does
asbuilt parses your repo, extracts every assumption the code makes about its
backend — ORM tables, Prisma schemas, raw SQL strings, Supabase calls, storage
buckets, deployed functions, auth providers, env vars — and verifies each one
against the live backend, with file:line provenance:
$ asbuilt check prod
target: prod risk: prod
❌ fail table_exists(table=invoices) [src/db.ts:10]
✅ pass table_exists(table=orders) [src/db.ts:6]
⏭️ skipped dynamic argument [src/db.ts:14]
note: RLS intent is not derived from code; verify explicitly with rls_enabled / policy_exists assertions.
note: 2 app-level env references were not checked — no connector exists for the application host.
summary: 1 pass, 1 fail, 0 unsupported, 1 skippedIn construction, the as-built drawings record what was actually built, as opposed to what the plans intended. Your code is the plan; your live backend is the as-built. Divergence between them is exactly where "the agent said done but production disagrees" lives.
Related MCP server: Architect-to-Product (A2P)
Install
Not on PyPI yet — install straight from source. As an MCP server for your coding agent (Claude Code shown; any MCP client works):
claude mcp add asbuilt -- uvx --from git+https://github.com/aniJani/asbuilt asbuiltOr classic:
pip install git+https://github.com/aniJani/asbuilt
claude mcp add asbuilt -- asbuiltOnce published to PyPI, these will work too:
claude mcp add asbuilt -- uvx asbuilt
# or
pip install asbuilt
claude mcp add asbuilt -- asbuiltTry it without a backend
No database, no MCP client, no credentials required — the built-in fixture
connector serves a recorded state file instead of a live backend, so you can
see the whole loop in three files.
mkdir -p demo/.asbuilt demo/srcdemo/.asbuilt/targets.json — a target backed by a local JSON file instead of
a real connection:
{"targets": {"demo": {"connector": "fixture", "statePath": "state.json", "risk": "dev"}}}demo/state.json — the "live" backend, recorded as a state doc (this is the
same shape a real connector like postgres or supabase returns):
{"connector": "fixture", "target": "demo", "sections": {"schema": {"hash": "x", "tables": [{"name": "orders", "columns": [], "indexes": [], "foreignKeys": []}]}}}demo/src/db.ts — a source file that assumes two tables, only one of which
exists in state.json:
import { createClient } from '@supabase/supabase-js'
const supabase = createClient(process.env.URL, process.env.KEY)
export const listOrders = () => supabase.from('orders').select('*')
export const listInvoices = () => supabase.from('invoices').select('*')Then run it:
cd demo && asbuilt check demotarget: demo risk: dev
❌ fail table_exists(table=invoices) [src/db.ts:4]
✅ pass table_exists(table=orders) [src/db.ts:3]
note: RLS intent is not derived from code; verify explicitly with rls_enabled / policy_exists assertions.
note: 2 app-level env references were not checked — no connector exists for the application host.
summary: 1 pass, 1 fail, 0 unsupported, 0 skippedasbuilt check reads .asbuilt/targets.json from the current directory
(or $ASBUILT_PROJECT_DIR if set) — that's where target and state-file
resolution happen. --repo <path> only changes which directory gets scanned
for code assumptions; it does not relocate where targets are read from.
Configure a target
.asbuilt/targets.json in your repo (env-var names only — secrets stay in
your environment):
{ "targets": {
"dev": { "connector": "supabase", "projectRef": "abc123",
"tokenEnv": "SUPABASE_ACCESS_TOKEN", "risk": "dev" },
"prod": { "connector": "postgres", "urlEnv": "PROD_PG_URL_RO", "risk": "prod" } } }Connectors: Postgres (anything speaking pg: RDS, Neon, Supabase, Azure), Supabase (schema/RLS + auth/storage/functions/secrets), Firebase (Firestore collections & indexes, security rules, auth, storage, functions — see docs/firebase-setup.md).
The tools
Tool | What the agent gets |
| the flagship: code assumptions vs. live state, with |
| live backend state, normalized |
| assert specifics: |
| blast-radius before a destructive op ("dropping |
| content-addressed, redacted state snapshots |
| structural diff between any two states (or live) |
Every response carries the target's risk tier (prod/staging/dev) — the
signal that was missing in both incidents above.
Honest by design
Read-only by construction. The connector protocol has no write method. Postgres opens
default_transaction_read_only=on. The Firebase connector probes its own credential and refuses to run if it holds write permissions.Unknown is never a pass. Assertions the connector can't evaluate return
unsupported; dynamic code references areskippedand listed — "checked 12, skipped 3" can never be misread as all-clear.Secrets never enter captured state. Values are hashed at capture time.
Benchmark
benchmark/ measures the failure mode this exists to prevent: how often agents
are confidently wrong about live infra, with vs. without verification.
15 seeded cases exercise all four MCP tools (deployment_check, infra_verify,
infra_impact, infra_drift) against the fixture connector, graded by
deterministic accept/reject regex pairs — no LLM judge. python -m benchmark.runner runs fully offline (no ANTHROPIC_API_KEY needed) and
doubles as the connector regression harness, printing each case's
tool-derived answer against its known-correct truth.
The agent-alone-vs-agent-with-tools model comparison (_run_comparison in
benchmark/runner.py) requires ANTHROPIC_API_KEY and hasn't been run live
yet — there is no published number for how much verification actually moves
agent accuracy. That comparison is implemented and offline-testable, but the
"agents are less confidently wrong with asbuilt than without it" claim is
still a hypothesis, not a result.
License
MIT
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