Password Strength Evaluator MCP. Calculate real-world password cracking difficulty instantly.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Password Strength Evaluator provides programmatic password auditing using the industry-standard zxcvbn engine. Pass any raw string to instantly get a security score, estimated crack time, and specific feedback on weaknesses like common dictionary words or patterns.
It moves credential validation beyond simple regex checks, giving SecOps agents true mathematical entropy data.
What your AI agents can do
Evaluate password
Takes a raw password string and returns its security score (0-4), estimated crack time, and specific weakness feedback for auditing user credentials.
Provides a quantifiable score (0-4) that measures the mathematical complexity and unpredictability of a given password.
Returns a concrete, estimated time an attacker would need to break the password using local hashing methods.
Provides detailed feedback on common flaws, such as dictionary words or predictable patterns, without needing complex custom rules.
Allows your agent to check a password against a minimum score threshold before allowing user creation or data submission.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Password Strength Evaluator: 1 Tool for Credentials
The single `evaluate_password` tool lets you score passwords mathematically, giving accurate entropy scores and estimated crack times for secure credential validation.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Password Strength Evaluator on Vinkius019e38d3evaluate password
Takes a raw password string and returns its security score (0-4), estimated crack time, and specific weakness feedback for auditing user credentials.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Password Strength Evaluator, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by zxcvbn. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 1 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Checking passwords used to mean writing complex, brittle regex rules.
Today, if you're building an auth flow, the manual process is often checking length, then requiring caps, then numbers. You write complicated regular expressions (regex) that validate character types. But this only checks for patterns; it doesn't check for actual security.
With the Password Strength Evaluator MCP Server, your agent just calls `evaluate_password`. It handles all the complex math—dictionary hits, pattern recognition, and entropy scoring—and spits out a real-world risk score you can act on.
The evaluate_password tool gives you actionable security data.
Before this server, if an audit failed, you often had to tell the user vaguely: 'Make it stronger.' The process was manual, requiring a human expert to interpret simple failure states and provide useful remediation advice.
Now, when `evaluate_password` runs, your agent reads the specific feedback—'Add another word or two. Uncommon words are better.' You eliminate guesswork entirely. You give users clear, actionable steps they can take right then.
What you can do with this MCP connector
The evaluate_password tool takes any raw password string and spits out its true security status for your agent to use. It moves credential validation way past simple checks—you don't need regex rules when you can get real mathematical entropy data. This isn't just another character counter; this is programmatic auditing using the industry-standard zxcvbn engine.
When your AI client handles user onboarding or audits stored credentials, subjective guessing doesn't cut it. You'll run into passwords that look complex but are mathematically weak. This server fixes that by giving you hard numbers and specific feedback on where they fail.
It calculates the quantifiable score—a 0-4 rating—that measures a password’s actual mathematical complexity and unpredictability. Getting this entropy score lets your agent immediately judge if a user's choice is strong enough for production use.
The tool estimates crack time, returning a concrete figure showing how long an attacker would realistically need to break the password using local hashing methods. This metric gives SecOps agents true risk data instead of just vague warnings. You know exactly what kind of effort they’re up against.
Beyond general scoring, the tool identifies specific weaknesses in the password. It details common flaws like dictionary words or predictable patterns without requiring you to write complex custom rules for every single flaw. This detailed feedback helps your agent tell users precisely why their password fails—like pointing out that 'password' is a known word.
With these data points, your agent can enforce actual security policies. You don't just ask the user to 'make it stronger'; you check the raw input against a minimum score threshold before allowing account creation or any kind of data submission. If the password doesn't hit that mark, the process stops.
By running evaluate_password on an initial string, your agent gets three key outputs: the overall security score (0-4), a concrete estimate for offline cracking time, and specific weakness feedback. This actionable data lets you guide users directly to better habits. For example, instead of just saying 'needs improvement,' your agent can read back, 'The score is 2; it's too predictable because it uses common words.'
This capability means your workflow isn't reliant on guesswork. You get objective proof of strength, quantifying the risk instantly so you can build real security guardrails directly into your client’s logic.
019e38d3-471a-702a-8372-cf61c80750a2 
How Password Strength Evaluator MCP Works
- 1 Pass the raw plaintext password string to the
evaluate_passwordtool. - 2 The server runs the input through the zxcvbn engine, analyzing its entropy and pattern matching.
- 3 You receive a structured output containing the security score, estimated crack time, and human-readable feedback.
The bottom line is you get rigorous mathematical validation of credentials that simple regex checks can't touch.
Who Is Password Strength Evaluator MCP For?
The SecOps Engineer tired of manually auditing user databases needs this. It’s for developers who build authentication flows and compliance officers needing proof that passwords meet actual security standards, not just corporate guidelines. If your job involves touching a credential field, you need it.
Uses the tool to audit stored plaintext credentials across databases and report true vulnerability scores for compliance reports.
Integrates evaluate_password into sign-up or password reset endpoints to enforce real-time security policies before saving a user record.
Runs batch checks on simulated credentials to prove that the application layer correctly rejects weak passwords based on industry standards.
What Changes When You Connect
- Stop trusting weak regex. The
evaluate_passwordtool uses dictionary and pattern matching to calculate true entropy, ensuring the passwords you accept aren't just long enough—they're actually secure. - You get concrete data on risk, not guesses. Instead of 'Password is weak,' the output gives an estimated crack time (e.g., '12 days'), which matters for incident response planning.
- Enforce policies programmatically. Your agent can check if a password meets your required score (like 3+) and automatically reject it, keeping your system compliant without manual checks.
- It works locally. The evaluation runs fast on the server side, meaning you don't send sensitive passwords to an external API just to check their strength.
- Audit entire user bases efficiently. By calling
evaluate_password, you can audit hundreds of stored credentials quickly, identifying systemic weaknesses across your platform.
Real-World Use Cases
New User Registration
A developer needs to ensure a new user's password meets policy. They call evaluate_password on the submitted string. If the score is below 3, the agent immediately prompts the user with specific feedback (e.g., 'Add another uncommon word') and blocks signup until compliance.
Database Audit
A SecOps engineer receives a dump of old credentials. The agent iterates through the list, calling evaluate_password for each one to quantify exactly which accounts are at high risk (low score/short crack time) and need immediate password resets.
Testing Password Resets
When a user clicks 'reset,' the system needs to enforce minimum strength. The agent calls evaluate_password on the temporary credential, checking if it passes the score threshold before allowing the final confirmation step.
The Tradeoffs
Relying only on length checks
The developer assumes that because a password is 12 characters long, it's safe. They implement simple if len(password) > 10 logic.
→
Don't trust length alone. Use the evaluate_password tool to get the actual entropy score and crack time. This tells you if a 12-character password is just dictionary words stuck together.
Using simple regex patterns
Implementing complex regex like [A-Z]{1}[a-z]{1}[0-9]{1} to enforce character variety.
→
Regex is too limited. Call evaluate_password instead; it uses advanced dictionary and pattern analysis, giving you a score that reflects real-world brute force difficulty.
Ignoring the feedback
The agent only checks if the password passes or fails a policy gate, but doesn't relay why it failed.
→
Always read the detailed feedback from evaluate_password. This tells you exactly what to tell the user: 'Add another word,' or 'Avoid common sequences.'
When It Fits, When It Doesn't
Use this server if your core task is validating password complexity and quantifying risk. You need a mathematical score, not just a pass/fail boolean. Use evaluate_password when you are enforcing minimum security policies on raw strings.
Don't use it if you need to build an entire multi-factor authentication (MFA) flow or integrate with external credential vaults—this tool is for pure validation. If your workflow requires coordinating multiple steps (like checking the password and verifying a user email), keep this server as one critical step within a larger agent pipeline.
Common Questions About Password Strength Evaluator MCP
Is the password sent to any API? +
No. The evaluation runs 100% local within the secure V8 Edge isolate, ensuring zero data leakage.
What is the score range? +
It returns a score from 0 (very weak) to 4 (very strong). We recommend rejecting any password with a score below 3.
Does it detect common patterns? +
Yes, it detects dates, names, sequential keyboard patterns (like 'qwerty'), and common dictionary words.
Does running `evaluate_password` send password data outside my environment? +
No. The evaluation runs locally on your agent's client side using the zxcvbn engine. This means raw passwords never leave your system, keeping them private.
What happens if I pass empty or non-string data to `evaluate_password`? +
It handles bad inputs gracefully. If you provide null or malformed input, the tool won't crash; it will return a specific low score and feedback stating that the input was invalid.
How can I use the output of `evaluate_password` within an agent workflow? +
You get three key outputs: score, crack time, and detailed feedback. Your agent logic reads these metrics to enforce policies—for example, rejecting any password with a score below 3.
Are there rate limits when I call `evaluate_password` repeatedly for bulk auditing? +
Vinkius manages infrastructure scalability, but rapid-fire calls require careful handling in your code. For large batches, implement a controlled delay loop to prevent hitting system constraints.
Can `evaluate_password` handle passwords that contain special characters? +
Yes. The tool accepts the raw string input directly. It analyzes all standard ASCII and Unicode characters, giving an accurate entropy score no matter what symbols are used.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.