Deterministic Feature Flag Evaluator MCP, Ready to Go
Use this Connector with Claude or Cursor to ensure consistent feature rollouts and prevent session flicker using deterministic hashing.
No credit card required. Experience the power of this integration risk-free.
Ensure consistent feature rollouts and stable user experiences.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Deterministic Feature Flag Evaluator Connector?
Average time for the server to become ready for requests over the last 2 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this Connector on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with 3 tools in Deterministic Feature Flag Evaluator for infrastructure
Run flag evaluations, check configurations, and verify rollout distributions directly through your AI agent.
Evaluate flag
Checks if a specific user meets the criteria for an enabled feature. It prevents unexpected UI changes during testing.
Get flag config
Pulls the current rules and settings for any flag key. This helps you understand exactly how a rollout is configured.
Verify distribution
Tests whether your percentage-based rollouts are distributed uniformly. It uses sampling to find deviations in your deployment.
A Connector is a URL. Vinkius runs it: hosting, security, governance, observability.
You're looking at one of 5,800+ managed Connectors. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Stop feature flicker with Deterministic Feature Flag Evaluator
For engineers and DevOps professionals who need to ensure feature consistency during canary deployments or gradual rollouts.
DevOps Engineer
Verifying that a 5% rollout is actually hitting exactly 5% across the population.
Frontend Developer
Checking if a new UI component is correctly assigned to specific beta testers during development.
QA Engineer
Testing edge cases in feature logic without manually toggling flags in a dashboard.
Frequently Asked Questions
How does Deterministic Feature Flag Evaluator prevent UI flickering? +
It uses CRC32 hashing to ensure that a user's assignment is tied to their ID. This means the result stays identical every time they load the page.
Can I use this Connector with Cursor or VS Code? +
Yes, it works with any MCP-compatible client including Cursor, VS Code, and Claude Desktop.
How do I check if my 20% rollout is actually uniform? +
You can simply ask your agent to run a distribution test using the verify_distribution tool on your specific flag.
Does this tool work for any user ID? +
Yes, as long as you provide the user ID, the hashing logic will deterministically calculate their assignment status.
Can I see the specific rules for a flag? +
You can use the configuration tool to pull all active rules and rollout percentages directly into your chat window.
How does the system ensure a user doesn't see a feature flicker? +
The system uses CRC32 hashing of the flagKey and userId. Since these inputs are immutable for a specific user and flag, the resulting hash value remains constant, ensuring the feature state does not change between sessions.
Can I bypass the percentage rollout for specific users? +
Yes. By providing an allowedIds list within the rulesJson parameter of the evaluate_flag tool, you can explicitly enable a feature for specific users regardless of the percentage rollout.
How do I verify if my rollout is actually distributed correctly? +
You can use the verify_distribution tool. By specifying a target percentage and a sample size, the tool will simulate users and report if the observed distribution matches your expected rollout percentage.
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