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A 75-line Python server built on FastMCP can scan text for a few fixed PII and prompt-injection patterns and return a block, review, or no-match result with a masked preview, all before your application sends anything to a model. That is a useful preflight check for an application. It is not a PII detector, and a clean result does not mean the text is safe. The decision to stop a send belongs to the host application, which has to require the scan and enforce its result.
What the 75-line server actually checks
The walkthrough, published on DEV Community on September 29, 2026 by AI Dev Hub, builds a small server with FastMCP imported from mcp.server.fastmcp. It takes an input string and runs it against three hard-coded patterns. Each pattern carries its own action, and that action is what drives the gate.
The three rules
| Rule | What it matches | Action when it fires | What it does not establish |
|---|---|---|---|
| An email-shaped string | review | The regex is deliberately incomplete and can match strings that are not real mailboxes. | |
| US SSN | A number in the US Social Security Number shape | review | It recognizes the format only. It does not tell you whether the number was ever issued. |
| Instruction override | A set of phrases associated with overriding prior instructions | block | It is a phrase match. Different wording can evade it. |
Because the rules are fixed and few, the server’s coverage is exactly what the table shows. Anything outside those shapes is invisible to it, including names, street addresses, account numbers in other formats, and most non-English phrasing of an override attempt.
What the server returns
Each call returns a structured result that the host can act on without reading the raw text again:
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- Status: one of
block,review, orno_match. - Reason: a short explanation of why the status was set.
- Rule names and character offsets: which rules fired and where each match starts and ends in the input.
- Masked preview: the original text with each matched span replaced by asterisks, so a reviewer can see the shape of what was flagged without seeing the value.
When the status is block, the most severe rule that fired determines the outcome. Because the preview only masks spans the rules found, it is not a redacted copy of the text. Treat it as a display aid, not as a sanitized payload to send onward.
Input caps and what happens at the limit
The sample sets two ceilings: 8,192 characters of input and 48 findings per call. These are constants written into the example code. The article does not measure performance at these values, and you should tune them to your own message sizes.
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When either cap is reached, the server returns review and omits the preview entirely. That is a sensible fail-safe, because a truncated scan could otherwise look like a clean one. Test both caps deliberately, since a long paste or a text with many short matches is the most likely way to hit them in production.
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Build your product language around that boundary. Describing the output as “verified clean” or “PII-free” overstates what the scan checked.
Make the scan mandatory in the host application
The server cannot stop a model request by itself. A tool call is an option the host can take or skip, so if the agent can reach the model without calling the tool, the check is optional. The article’s own wording is “The host owns the gate.” Put the check where the application is forced through it:
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- Locate the single function or code path that sends user content to an external model.
- Call the scan on that content inside that path, before the network request is built.
- If the status is
block, stop the send and return an error to the caller without transmitting the text. - If the status is
review, also hold the send. Route it to whatever human or policy step your application uses to release it. - Only transmit when the status is
no_match, and log the status and rule names rather than the input. - Make sure no alternate code path, such as a retry, a background job, or a second model client, skips step 2.
Local execution does not make the text harmless
The walkthrough runs as a local subprocess that talks to the host over stdio. It needs no model call, no API token, and no paid service. That keeps the scanner itself off the network, but it does not protect the data around it:
- The host process necessarily reads the full text to pass it to the tool.
- Some hosts can export tool-call traces to a remote service. Check your host’s logging and trace settings before you run real customer data through the scan.
- The masked preview can still contain PII that the rules did not catch, so it should be handled with the same care as the original text.
The tutorial’s practical suggestions fit with this. Keep raw matches out of ordinary debug logs. Use synthetic fixtures when you experiment with rule changes. Keep any protocol debug output off stdout, because stdio transport uses stdout for protocol messages. Test the cap and overlap cases. Finally, treat the scan as one layer of prompt-injection defense, alongside restricted tool permissions and human approval for consequential actions.
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Check the SDK version before you copy the code
The sample uses the v1-era FastMCP import and API. The official MCP Python SDK documentation, as of October 2026, labels v2 as the current stable line and documents MCPServer as the server class. Before you treat the sample as drop-in code:
- Confirm your Python is 3.10 or newer, which the official SDK page states as its requirement.
- Read the SDK’s migration guide and map the
FastMCPcalls to their v2 equivalents. - Pin the SDK version in your dependency file so an upgrade cannot change server behavior silently.
- Confirm which transport you need. The official SDK describes stdio, Streamable HTTP, and SSE.
Choosing how to expose the scan
The same scan logic can be exposed three ways. The transport changes how callers reach the check, not how good the check is.
| Option | Good fit | Boundary and data path | Main cost |
|---|---|---|---|
| Local MCP tool | An MCP-compatible host needs a named scan tool it can discover | Host and subprocess permissions; text passes through local pipes | Host setup and protocol handling |
| Plain Python function | One Python application owns the whole send path | Runs with the application’s existing permissions, in the same process | Other hosts cannot discover it as a tool |
| Operated HTTP endpoint | Several applications need one shared rules service | Text crosses the network; you must implement authentication | Deployment and ongoing service operations |
A plain function is the simplest choice when one caller owns the path. MCP makes sense when several compatible hosts need the same named tool. An HTTP service is worth its operational cost only when many applications must share one rules set and you are prepared to secure it.
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