secretscan
secretscan-mcp-server
Ein MCP-Server, der einen Diff, den Inhalt einer Datei oder ein eingefügtes Snippet auf versehentlich committete Geheimnisse scannt – sodass jeder MCP-kompatible Agent seine eigenen Änderungen selbst überprüfen kann, bevor er committet, einen PR eröffnet oder einen Protokollauszug irgendwo einfügt. Vollständig lokal: kein API-Schlüssel, kein Netzwerkaufruf, keine Abhängigkeit außer dem MCP SDK und Zod.
Warum
Ein geleakter Zugangsdatensatz ist eine der häufigsten realen Ursachen für Kompromittierung von Konten und Infrastruktur, und einer der einfachsten Fehler in einem großen Diff – ein Debug-console.log mit einem echten API-Schlüssel, eine versehentlich committete Konfigurationsdatei, eine in einen Chat eingefügte .env-Datei, während man um Hilfe bittet. Ein Agent, der kurz davor steht, zu committen, zu pushen oder Text zu teilen, ist gut positioniert, dies bevor es passiert zu erkennen, wenn er eine kostengünstige Prüfmöglichkeit hat.
Related MCP server: netallion-mcp-lite
Werkzeug
scan_for_secrets
Scannt Text anhand eines festen Satzes von Anbieterformat-Signaturen sowie einer generischen, entropiebasierten Heuristik:
Detektor | Erkennt |
| AWS-Zugriffsschlüssel-IDs ( |
| AWS-Geheimschlüssel, wenn kontextuell beschriftet |
| GitHub-PATs ( |
| Slack-Tokens ( |
| Stripe-Geheim-/veröffentlichbare Schlüssel |
| Google-API-Schlüssel ( |
| npm-Veröffentlichungstokens ( |
| PEM-Private-Key-Blöcke |
| JSON-Web-Tokens |
| alles, was einer geheim klingenden Variablennamen zugewiesen ist ( |
Ergebnisse werden immer geschwärzt – AKIAIOSFODNN7EXAMPLE wird als AKIA************MPLE (20 Zeichen) gemeldet, niemals vollständig. Die eigene Ausgabe des Tools kann daher bedenkenlos protokolliert oder angezeigt werden, ohne das Gefundene weiterzuverbreiten.
Dies ist ein Scan mit festen Mustern und Heuristik, nicht erschöpfend. Ein sauberes Ergebnis bedeutet „kein bekanntes Muster verglichen", nicht „definitiv sicher" – die Ausgabe der Anwendung sagt dies ausdrücklich.
Installieren und konfigurieren
git clone https://github.com/wedo911/secretscan-mcp-server.git
cd secretscan-mcp-server
npm install
npm run buildFügen Sie es zur Konfiguration Ihres MCP-Clients hinzu (z. B. claude_desktop_config.json oder die .mcp.json eines Projekts für Claude Code):
{
"mcpServers": {
"secretscan": {
"command": "node",
"args": ["/absolute/path/to/secretscan-mcp-server/dist/index.js"]
}
}
}Tests ausführen
npm run build
node --test tests/entropy.test.mjs tests/detectors.test.mjs24 Tests, einschließlich eines für jeden benannten Detektor, das Platzhalter- und Niedrigentropie-Filterverhalten des generischen Detektors und eine explizite Prüfung, dass geschwärzte Ausgaben niemals den vollständigen Geheimniswert enthalten.
Ohne Client ausprobieren
npx @modelcontextprotocol/inspector --cli node dist/index.js \
--method tools/call --tool-name scan_for_secrets \
--tool-arg text='aws_access_key_id = AKIAIOSFODNN7EXAMPLE'(Das ist AWS' eigener öffentlich dokumentierter Beispielschlüssel – keine echten Zugangsdaten.)
Lizenz
MIT – siehe LICENSE.
Available Tools
1 toolscan_for_secretsScan Text for Leaked SecretsARead-onlyIdempotent
Scan text (a git diff, a file's contents, a pasted snippet) for accidentally-committed secrets: cloud provider keys (AWS, Google), platform tokens (GitHub, Slack, npm), payment keys (Stripe), private key blocks, JWTs, and a generic entropy-gated check for anything assigned to a secret-sounding variable name. Use this before committing, opening a PR, or pasting logs/config anywhere -- a committed secret is one of the most common real-world causes of account and infrastructure compromise, and is easy to miss in a large diff.
Findings are always redacted (first/last few characters only) -- this tool never returns a full secret value, so its own output is safe to log or display.
Args:
text (string, 1-200000 chars): the text to scan.
Returns: For JSON format: { "found": boolean, "riskLevel": "none" | "medium" | "high" | "critical", "findings": [ { "detector": string, "category": string, "severity": "low"|"medium"|"high"|"critical", "line": number, "column": number, "redacted": string } ] }
Examples:
Use when: about to run "git commit" or "git push" -- scan the staged diff first
Use when: about to paste a config file, log excerpt, or error message into a chat or issue tracker
Don't use when: you need certainty nothing sensitive is present -- this is a fixed-pattern + heuristic scan, not exhaustive; a clean result means "no known pattern matched," not "definitely safe"
Error Handling:
Returns an error if text is empty or exceeds 200000 characters.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to scan -- a git diff, a file's contents, or any pasted snippet. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds critical behavioral context beyond this: findings are always redacted to the first/last few characters, output is safe to log, the scan is not exhaustive, and empty/oversized inputs produce errors. This meaningfully exceeds what annotations convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The definition is well-structured with clear sections (description, args, returns, examples, error handling), front-loads the core purpose and usage guidance, and every section carries useful information. Length is justified by the tool's security-critical nature and the need to explain redaction and limitations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description fully specifies the return payload structure including found, riskLevel, and findings fields. It also covers error handling, length constraints, and usage examples. For a single-parameter tool with strong annotations, this is complete and leaves no critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the input schema already describes text as 'a git diff, a file's contents, or any pasted snippet' with length constraints. The description repeats these details, adding little new semantic meaning beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource ('Scan text ... for accidentally-committed secrets') and enumerates the exact detector categories (cloud keys, platform tokens, payment keys, private key blocks, JWTs, entropy-gated checks). This is precise and unambiguous even without sibling tools to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use scenarios ('before committing, opening a PR, or pasting logs/config anywhere') and an explicit don't-use case ('you need certainty nothing sensitive is present'). It also honestly frames the tool's limits as a fixed-pattern + heuristic scan, giving clear decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
scan_for_secrets
TDQS
Scored across 1 tool
Only one tool exists, so there is zero ambiguity or risk of misselection. The tool's purpose is clearly singular.
The single tool uses a consistent verb_noun pattern ('scan_for_secrets'), which is clear and predictable.
With only one tool, the server feels thin, but for a narrow purpose like scanning text for secrets, a single comprehensive tool is reasonable. It borders on minimal but works.
The tool covers the full scanner domain: it detects a wide variety of secrets, provides redacted output, and handles error cases. There are no obvious missing operations for its stated purpose.
Maintenance
Related MCP Connectors
Scan configs, files, or text for leaked secrets and obvious misconfigurations. Nothing stored.
Scan text and git diffs for committed credentials using real vendor key formats plus entropy.
Deep security scans of repos you own from your editor: dependency CVEs, SAST, git-history secrets.
Risk-scan a diff, flag AI-generated-code tells, find secrets. 5 of 7 tools need no account.
Related MCP Servers
- AlicenseAqualityBmaintenanceEnables scanning diffs or code blobs for leaked secrets, returning a verdict with severity and masked findings, all processed locally with no data sent externally.1MIT
- AlicenseNot gradedqualityCmaintenanceScans text and files for common secrets (AWS, GitHub, etc.) and redacts them to prevent credential leakage in AI-assisted development. Runs entirely locally with no telemetry.24 PyPIMIT
- AlicenseAqualityDmaintenanceScans projects for hardcoded secrets, unprotected .env files, and console.log leaks to prevent credential exposure.526 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to scan source code for secrets like API keys and passwords locally without network requests.2Apache 2.0