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A 75-line Python MCP server can scan text for a few PII-shaped strings and a basic prompt-injection phrase before your application sends that text to a model. It is a useful preflight signal, not a privacy filter or a proof that a prompt is safe. The application has to require the scan and stop the send when the result is block or review.
What the tool actually does
The example comes from a September 29, 2026 DEV Community article by AI Dev Hub. It builds a local server with FastMCP and exposes one scan tool. That tool checks an input string against three fixed patterns and returns a structured result.
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The three rules
| Rule | What it looks for | Action returned |
|---|---|---|
| An email-shaped string | review |
|
| US SSN | A nine-digit SSN-shaped number | review |
| Instruction override | A phrase-based attempt to override prior instructions | block |
What comes back
Each response includes a status (block, review, or no_match), a human-readable reason, the names of the rules that fired, and character offsets for each finding. It also includes a preview in which matched positions are replaced with asterisks. The offsets let your application show a reviewer exactly where the match sits without printing the raw value into a log line.
Limits built into the example
The sample sets two caps: an input limit of 8,192 characters and a findings limit of 48. When either cap is reached, the tool returns review and omits the preview. These are constants chosen in the example code, not measured performance figures. Your own limits should reflect your payload sizes and latency budget.
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What a no_match result does not establish
The author states the central caution directly: the tool “can’t prove that text is safe, and its prompt injection check is only a tripwire.” Each rule has its own blind spots:
- Email: the pattern is deliberately incomplete and can match strings that are not real mailboxes.
- SSN: the pattern recognizes a shape. It cannot tell whether a number was ever issued or belongs to a person.
- Injection: a differently worded attack can evade a phrase match entirely.
- Everything else: names, street addresses, account numbers, medical identifiers, and any PII format outside the three rules are not checked.
So no_match means only that these three rules found nothing. It should never be logged or displayed as “clean” or “safe.”
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Making the gate enforceable
The tutorial’s most important design point is that the host owns the gate. An MCP tool call does not, by itself, stop a later model request. If the agent or calling code can skip the scan, the check is optional, and optional checks tend to be skipped under load or in edge cases.
Place the scan in the application’s required request path, before any transmission to an external model. Then treat the outcomes as follows:
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- no_match: the request may proceed to the model call.
- review: hold the request. Show the masked preview to a reviewer, or reject it, according to your policy.
- block: stop the request. Do not offer a send-anyway path in the same flow.
The tutorial treats both block and review as stops. Relaxing review into a warning that users can dismiss turns the check into decoration.
Data handling before you use real text
The walkthrough runs as a local subprocess that communicates over stdio. It needs no model call, no API token, and no paid service. That local design matters, but it does not make the text harmless:
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- The host process receives the full input text, so anything the host logs is a potential PII leak.
- Some hosts export tool traces remotely. Check that behavior before sending real customer data through the tool.
- The masked preview still contains everything the rules missed. Masking is not redaction of unknown PII.
The author’s guidance, which is design advice rather than independently validated testing, includes these practices:
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- Keep raw matches out of ordinary debug logs. Log rule names and offsets instead.
- Use synthetic fixtures when experimenting with rule changes.
- Keep protocol debug output off stdout, because stdio transport uses stdout for protocol messages.
- Write tests for the 8,192-character and 48-finding caps, and for overlapping matches.
- Add prompt-injection defenses beyond the scan. Restrict tool permissions and require approval for consequential actions.
Compatibility with the current MCP Python SDK
The tutorial’s code uses the v1-era FastMCP import and API. The official MCP Python SDK documentation, as checked for this article, labels v2 as the current stable line and documents MCPServer as the server class. The SDK requires Python 3.10 or later and supports stdio, Streamable HTTP, and SSE transports.
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Before you copy the tutorial’s code into a project, read the SDK migration guide, then either pin the older SDK version the example expects or port the server to the current API. Do not assume the sample runs unchanged on the latest release.
Choosing how to expose the check
The same scan logic can sit behind three different interfaces. The transport changes how callers reach the check. It does not change scan quality.
| Option | Good fit | Data path and boundary | Main cost |
|---|---|---|---|
| Local MCP tool | An MCP host needs a named scan tool that other compatible clients can discover | Host and subprocess permissions; text passes through a local stdio pipe | Host setup and protocol handling |
| Plain Python function | One Python application owns the whole request flow | Runs inside the application’s existing permissions and process | No tool discovery for other hosts |
| Operated HTTP endpoint | Several applications need one shared rules service | Network request; you must implement authentication yourself | Deployment, access control, and service operations |
For a single caller, a plain function is the simplest starting point. MCP becomes worthwhile when several compatible hosts need the same named tool. An HTTP endpoint makes sense when a shared service must be operated centrally and the network boundary is part of your security design.
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A practical rollout sequence
- Run the scan in a test environment with synthetic data only, including inputs at and just above both caps.
- Wire the four outcomes (
no_match,review,block, and cap-triggeredreview) into your request path, and confirm that no code path reaches the model without passing the scan. - Audit every logger, trace exporter, and error handler that can receive the input text or the tool response.
- Decide who reviews
reviewresults and how quickly, since held requests will accumulate. - Only then evaluate real data, under your own privacy and contractual obligations.
The tutorial shows how to build a fast, inspectable preflight check in a few dozen lines. Whether that check is adequate for your data is a separate question that the example cannot answer.
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