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.
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
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/aniJani/asbuilt'
If you have feedback or need assistance with the MCP directory API, please join our Discord server