Bring Feature Flags
to Pydantic AI
Learn how to connect Unleash (Feature Toggles) to Pydantic AI and start using 11 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Unleash (Feature Toggles) MCP Server?
Connect your Unleash instance to any AI agent and gain full control over your feature management lifecycle through natural conversation.
What you can do
- Feature Evaluation — Fetch all feature flags and strategies for server-side evaluation or evaluate specific flags for client-side contexts using User IDs and properties.
- Project & Environment Audit — List all Unleash projects, environments, and segments to understand your infrastructure layout.
- Flag Management — Inspect all feature flags within specific projects to verify rollout statuses and strategy configurations.
- Metrics & Registration — Report SDK usage metrics and register new client or frontend instances directly through the agent.
- User Management — Retrieve lists of users and segments to verify targeting rules and access.
How it works
- Subscribe to this server
- Enter your Unleash API URL and API Token (Admin, Client, or Frontend depending on your needs)
- Start managing your feature rollouts from Claude, Cursor, or any MCP-compatible client
Who is this for?
- DevOps & SREs — quickly audit environments and segments without navigating the Unleash UI
- Product Managers — check the status of feature toggles and rollout strategies across different projects
- Software Engineers — verify flag evaluations and context properties directly from the code editor
Built-in capabilities (11)
Fetch all feature flags and strategies for server-side evaluation
Optionally provide context like userId or properties. Fetch enabled feature flags for a specific Unleash Context
Fetches all environments configured in Unleash. List all Unleash environments
Fetches features for a given project ID. List all feature flags in a specific project
Fetches all projects configured in Unleash. List all Unleash projects
Fetches all segments configured in Unleash. List all Unleash segments
Fetches all users configured in Unleash. List all Unleash users
Register a new backend SDK instance
Register a new frontend SDK instance
Report flag usage metrics from a backend SDK
Report flag usage metrics from a frontend SDK
Why Pydantic AI?
Pydantic AI validates every Unleash (Feature Toggles) tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Unleash (Feature Toggles) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Unleash (Feature Toggles) connection logic from agent behavior for testable, maintainable code
Unleash (Feature Toggles) in Pydantic AI
Unleash (Feature Toggles) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Unleash (Feature Toggles) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Unleash (Feature Toggles) in Pydantic AI
The Unleash (Feature Toggles) MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 11 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Unleash (Feature Toggles) for Pydantic AI
Every tool call from Pydantic AI to the Unleash (Feature Toggles) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I evaluate feature flags for a specific user ID?
Yes. Use the get_frontend_features tool and provide the userId. The agent will return the enabled flags based on the Unleash context for that specific user.
How do I see all feature flags associated with a specific project?
You can use the list_project_features tool by providing the projectId. This will list all toggles, their types, and current statuses within that project.
Does this server support listing segments and environments?
Yes, the server includes list_segments and list_environments tools, allowing you to audit your Unleash configuration and targeting rules easily.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Unleash (Feature Toggles) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
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Update: pip install --upgrade pydantic-ai
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