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GetStream MCP Server for CrewAIGive CrewAI instant access to 23 tools to Add Activity To Feed, Add To Collection, Batch Delete Collections, and more

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Connect your CrewAI agents to GetStream through Vinkius, pass the Edge URL in the `mcps` parameter and every GetStream tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Ask AI about this MCP Server for CrewAI

The GetStream MCP Server for CrewAI is a standout in the Productivity category — giving your AI agent 23 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python
from crewai import Agent, Task, Crew

agent = Agent(
    role="GetStream Specialist",
    goal="Help users interact with GetStream effectively",
    backstory=(
        "You are an expert at leveraging GetStream tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in GetStream "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 23 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
GetStream
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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

About GetStream MCP Server

Connect GetStream to your AI agent to orchestrate complex social architectures and activity feeds using natural language.

When paired with CrewAI, GetStream becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call GetStream tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

  • Feed Management — Retrieve, add, or remove activities from specific feed slugs and user timelines using get_feed and add_activity_to_feed.
  • Activity Orchestration — Update activity metadata or perform partial updates to specific fields via partial_update_activity without rewriting entire objects.
  • Social Graph — Manage follower relationships, list who follows a feed, and perform follow operations using follow_feed and list_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.

The GetStream MCP Server exposes 23 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 23 GetStream tools available for CrewAI

When CrewAI connects to GetStream through Vinkius, your AI agent gets direct access to every tool listed below — spanning activity-feeds, chat-api, social-infrastructure, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

add

Add activity to feed on GetStream

Add an activity to a feed

add

Add to collection on GetStream

Add objects to a collection

batch

Batch delete collections on GetStream

Batch delete collections

batch

Batch follow on GetStream

Batch follow multiple feeds

batch

Batch get collections on GetStream

Batch retrieve collections

batch

Batch post collections on GetStream

Batch create/update collections

delete

Delete collection object on GetStream

Delete an individual collection object

delete

Delete file on GetStream

Delete a file by URL

follow

Follow feed on GetStream

Follow a target feed

get

Get activities on GetStream

Retrieve specific activities by ID or foreign ID

get

Get collection object on GetStream

Retrieve an individual collection object

get

Get feed on GetStream

Supports pagination. Retrieve activities in a feed

get

Get open graph on GetStream

Scrape Open Graph data from a URL

list

List feed followers on GetStream

List feeds following this feed

list

List feed follows on GetStream

List feeds this feed follows

partial

Partial update activity on GetStream

Partially update activity data

process

Process image on GetStream

Process or resize an image

remove

Remove activity from feed on GetStream

Remove an activity from a feed

unfollow

Unfollow feed on GetStream

Unfollow a target feed

update

Update activities on GetStream

Update activity metadata

update

Update collection object on GetStream

Update an individual collection object

upload

Upload file on GetStream

Upload a file

upload

Upload image on GetStream

Upload an image

Connect GetStream to CrewAI via MCP

Follow these steps to wire GetStream into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install CrewAI

Run pip install crewai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
03

Customize the agent

Adjust the role, goal, and backstory to fit your use case
04

Run the crew

Run python crew.py. CrewAI auto-discovers 23 tools from GetStream

Why Use CrewAI with the GetStream MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with GetStream through the Model Context Protocol.

01

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

02

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

03

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

04

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

GetStream + CrewAI Use Cases

Practical scenarios where CrewAI combined with the GetStream MCP Server delivers measurable value.

01

Automated multi-step research: a reconnaissance agent queries GetStream for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

02

Scheduled intelligence reports: set up a crew that periodically queries GetStream, analyzes trends over time, and generates executive briefings in markdown or PDF format

03

Multi-source enrichment pipelines: chain GetStream tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

04

Compliance and audit automation: a compliance agent queries GetStream against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for GetStream in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with GetStream immediately.

01

"Show me the latest activities in the 'user' feed for user ID 'user_123'."

02

"Make the feed 'timeline:alice' follow 'user:bob'."

03

"Update activity ID 'act_999' to set the 'is_featured' field to true."

Troubleshooting GetStream MCP Server with CrewAI

Common issues when connecting GetStream to CrewAI through Vinkius, and how to resolve them.

01

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
02

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
03

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
04

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

GetStream + CrewAI FAQ

Common questions about integrating GetStream MCP Server with CrewAI.

01

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
02

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
03

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
04

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
05

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

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