About Deliverd Screen
Deliverd Screen is a local text screening package for teams and developers who want to check prompts before sending them to an AI model. It detects identifiers, secrets, and custom terms, replaces matches with reversible placeholders, and provides an MCP tool, CLI, and integrations.
Key features
- 01Detect personal data and secrets
- 02Redact matches with reversible placeholders
- 03Add custom confidential terms
- 04Provide CLI and pre-commit or CI checks
- 05Integrate with Vercel AI SDK and optional Purview
Use cases
- 01Redact identifiers before sending prompts to a model
- 02Keep secrets out of agent context with MCP
- 03Check documents in pre-commit or CI
- 04Apply custom terms and optional Purview rules
MCP support
The MCP server exposes a `screen_text` tool that returns a verdict and redacted text without returning replaced values. It can run optional Microsoft Purview checks when configured.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
1Saved tool list · updated 3d ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| text | string | Yes | The text to check. |
| terms | array | No | Your own words not to send: clients, code names. |
| personalData | boolean | No | Whether the destination may receive personal data. Default false. |
| classification | enum | No | The highest classification the destination may receive. Default internal.public | internal | confidential | restricted |
Details
From the project READMEDeliverd Screen
A pre-send check for every prompt. Find and redact personal data, secrets and your own confidential terms before text goes to an AI model.
- Local. No account, no key, no network call. The text never leaves your process, unless you turn on the optional Microsoft Purview check, which sends it to your own Microsoft tenant and nowhere else.
- Reversible. Values become placeholders such as
[EMAIL_1](the same value gets the same placeholder throughout), andrestore()puts them back in the model's answer. - Bring your own model for names. Patterns can't find people's names, but a model can. Plug in a Hugging Face token classifier, Presidio, or any model with the same output.
- No dependencies. MIT.
npm install @deliverd/screen
import { screen } from "@deliverd/screen";
const checked = await screen("Reply to [email protected] about IBAN IE29AIBK93115212345678");
checked.verdict; // "redact"
checked.text; // "Reply to [EMAIL_1] about IBAN [IBAN_1]"
const answer = await model.generate(checked.text);
checked.restore(answer); // the real values, back in the answer
What it finds
Email addresses, phone numbers, IBANs, payment card numbers (Luhn-checked),
UK National Insurance numbers, Irish PPS numbers (check character), US Social
Security numbers, IP addresses (v4 and v6), dates of birth, API keys and tokens
(AWS, GitHub, Slack, Stripe, Anthropic, OpenAI, Google, JWTs and more), private
keys, passwords in connection strings and .env files, words
that mark special-category data such as health or religion, and your own
terms.
The four answers
By default the model may receive internal data and no personal data. Pass
allow: { personalData: true, classification: "confidential" } for a model you
have approved for more.
| Verdict | What to do |
|---|---|
allow | Send text, which is the original. |
redact | Send text, which has placeholders in place of what the model may not receive. |
ask | A person has to decide (special-category data, by default). allowed is false. |
deny | Send nothing. Secrets are refused by default. |
Names: plug in a model
import { pipeline } from "@huggingface/transformers";
import { tokenClassifier, presidio } from "@deliverd/screen/adapters";
const ner = await pipeline("token-classification", "Xenova/bert-base-NER");
const checked = await screen(prompt, { detectors: [tokenClassifier((t) => ner(t))] });
// or a Presidio analyzer you run yourself:
// docker run -p 5002:3000 mcr.microsoft.com/presidio-analyzer
await screen(prompt, { detectors: [presidio({ url: "http://localhost:5002" })] });
A detector is a function from the text to { kind, start, end } spans, so any
model works, including Perplexity's PII-Tracer and GLiNER. Check a model's
licence before you ship it. Run it where the text already is: a detector that
sends the text to a hosted service has already sent it.
Vercel AI SDK
import { generateText, wrapLanguageModel } from "ai";
import { screenMiddleware } from "@deliverd/screen/ai-sdk";
const model = wrapLanguageModel({ model: openai("gpt-5"), middleware: screenMiddleware() });
const { text } = await generateText({ model, prompt });
The middleware screens the whole conversation at once, so one value keeps one
placeholder across messages. It restores the answer, including a streamed one where a
placeholder arrives split across chunks. On a deny or an ask it throws
ScreenBlockedError and the model is never called. It works with AI SDK 7 by
default; pass specificationVersion: "v3" for AI SDK 6.
CLI, pre-commit and CI
cat prompt.txt | npx @deliverd/screen --redact # the redacted text
npx @deliverd/screen --json notes.txt # kinds and offsets, never values
npx @deliverd/screen --fail evals/*.jsonl # exit 1 if anything is found
npx @deliverd/screen --terms clients.txt doc.md # your own words, one per line
In GitHub Actions, uses: deliverd-dev/deliverd-screen@v0 with paths: runs
the same check; for pre-commit, the hook id is deliverd-screen. Both are in
deliverd-dev/deliverd-screen.
MCP
claude mcp add deliverd-screen -- npx -y @deliverd/screen mcp
This gives an agent in Claude Code, Cursor or Codex one tool, screen_text.
It returns the verdict and the redacted text, and never the values it
replaced, so they don't enter the agent's context through the tool.
Microsoft Purview
If your organisation writes its data loss prevention rules in Microsoft Purview, the same rules can apply here, for any model and any tool. Pass a Purview check and the stricter answer wins: a Purview block denies, and a warn is an ask.
import { entraAppToken, purviewCheck, screen } from "@deliverd/screen";
const purview = purviewCheck({
token: entraAppToken({ tenantId, clientId, clientSecret }),
userId: entraObjectIdOfThePerson,
app: { name: "Claims assistant", entraAppId: clientId },
});
const checked = await screen(prompt, { purview });
This is the one thing in the package that uses the network, and only when you
pass it: the text goes to Microsoft Graph (processContent), in your own
tenant, and nowhere else. The Entra app needs Content.Process.All, and the
DLP rule must be created with New-DlpComplianceRule (Purview does not apply a
portal-only rule to an Entra-registered app).
For the MCP server, set DELIVERD_PURVIEW_TENANT_ID,
DELIVERD_PURVIEW_CLIENT_ID, DELIVERD_PURVIEW_CLIENT_SECRET and
DELIVERD_PURVIEW_USER_ID (and optionally DELIVERD_PURVIEW_ON_UNAVAILABLE:
allow, ask or deny).
A net, not a certificate
Structured identifiers are found well. Names are found only as well as the model you plug in. Redacted text about a person is still personal data under GDPR. Use this to send fewer identifiers, never as proof that a prompt was clean.
When a person has to decide
A library can only tell you to stop. Deliverd's
Data Gate makes the call. deliverd.screen() in @deliverd/sdk runs these
same detectors on your machine and sends Deliverd only the counts ("two email
addresses, one IBAN"). The verdict comes back from your organisation's policy and from what
its AI register approves each model to receive. On an ask it puts the
question to the right person in Slack, Teams or on their phone, and every check is
recorded. It takes the same detectors.
Licence
MIT. The detectors are shared with Deliverd's SDKs and held to one set of test
vectors, packages/conformance/screening.json.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.