Bring Activity Feeds
to Pydantic AI
Learn how to connect GetStream to Pydantic AI and start using 23 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 GetStream MCP Server?
Connect GetStream to your AI agent to orchestrate complex social architectures and activity feeds using natural language.
What you can do
- Feed Management — Retrieve, add, or remove activities from specific feed slugs and user timelines using
get_feedandadd_activity_to_feed. - Activity Orchestration — Update activity metadata or perform partial updates to specific fields via
partial_update_activitywithout rewriting entire objects. - Social Graph — Manage follower relationships, list who follows a feed, and perform follow operations using
follow_feedandlist_feed_followers. - Collections & Files — Handle collection objects and manage file/image uploads for rich media experiences.
- Open Graph — Retrieve Open Graph data for URLs to enrich activity content automatically.
How it works
- Subscribe to this server
- Enter your Stream API Key and JWT Token
- Start managing your social infrastructure from any MCP-compatible client
Who is this for?
- Product Managers — monitor feed health and activity patterns without technical dashboards.
- Developers — test feed logic and activity updates directly from the IDE to speed up social feature integration.
- Community Managers — manage social graphs and moderate feed content through natural conversation.
Built-in capabilities (23)
Add an activity to a feed
Add objects to a collection
Batch delete collections
Batch follow multiple feeds
Batch retrieve collections
Batch create/update collections
Delete an individual collection object
Delete a file by URL
Follow a target feed
Retrieve specific activities by ID or foreign ID
Retrieve an individual collection object
Supports pagination. Retrieve activities in a feed
Scrape Open Graph data from a URL
List feeds following this feed
List feeds this feed follows
Partially update activity data
Process or resize an image
Remove an activity from a feed
Unfollow a target feed
Update activity metadata
Update an individual collection object
Upload a file
Upload an image
Why Pydantic AI?
Pydantic AI validates every GetStream tool response against typed schemas, catching data inconsistencies at build time. Connect 23 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
- —
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your GetStream integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
- —
Dependency injection system cleanly separates your GetStream connection logic from agent behavior for testable, maintainable code
GetStream in Pydantic AI
GetStream and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect GetStream 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 GetStream in Pydantic AI
The GetStream 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 23 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
GetStream for Pydantic AI
Every tool call from Pydantic AI to the GetStream MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I retrieve specific activities using their unique identifiers?
Yes. Use the get_activities tool by providing a comma-separated list of activity IDs or foreign IDs to fetch their full metadata.
How do I check which feeds a specific user is currently following?
You can use the list_feed_follows tool. Provide the feed slug and user ID to get a comprehensive list of all target feeds being followed.
Is it possible to update only a single field in an activity without sending the whole object?
Absolutely. The partial_update_activity tool allows you to set or unset specific fields on an activity using a JSON payload, preserving the rest of the data.
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 GetStream MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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