mcpField
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Deliverd Screen

Deliverd Screen

Local
deliverd.devSecurity1Updated today

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

  1. 01Detect personal data and secrets
  2. 02Redact matches with reversible placeholders
  3. 03Add custom confidential terms
  4. 04Provide CLI and pre-commit or CI checks
  5. 05Integrate with Vercel AI SDK and optional Purview

Use cases

  1. 01Redact identifiers before sending prompts to a model
  2. 02Keep secrets out of agent context with MCP
  3. 03Check documents in pre-commit or CI
  4. 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.

mcp.json · Universal config
{
"mcpServers": {
"deliverd-dev-deliverd-screen": {
"command": "npx",
"args": [
"-y",
"@deliverd/screen"
]
}
}
}
Run commandnpx -y @deliverd/screen

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@deliverd/screen0.2.0

Tools

1

Saved tool list · updated 3d ago

ParameterTypeRequiredDescription
textstringYesThe text to check.
termsarrayNoYour own words not to send: clients, code names.
personalDatabooleanNoWhether the destination may receive personal data. Default false.
classificationenumNoThe highest classification the destination may receive. Default internal.public | internal | confidential | restricted

Details

From the project README

Deliverd 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), and restore() 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.

VerdictWhat to do
allowSend text, which is the original.
redactSend text, which has placeholders in place of what the model may not receive.
askA person has to decide (special-category data, by default). allowed is false.
denySend 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

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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.