How do I find my Fibery Workspace name?

Your workspace name is the subdomain in your Fibery URL. For example, if you access Fibery at `https://my-team.fibery.io`, your workspace name is `my-team`.

How do I generate a Fibery API Token?

In your Fibery workspace, go to **Settings > API**, click on **Create New Token**, give it a name, and copy the generated token immediately.

Can I query custom fields created in my spaces?

Yes! Use the `get_schema` tool to see the fully qualified names of your custom fields, and then include them in the `select_json` parameter of the `query_entities` tool.

Fibery MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: Fibery
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Fibery Specialist",
    goal="Help users interact with Fibery effectively",
    backstory=(
        "You are an expert at leveraging Fibery 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 Fibery "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Ed25519Audit chain

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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 Fibery MCP Server

Fibery is a work management platform that adapts to your unique processes. This MCP server allows your AI agent to interact with your Fibery workspace seamlessly.

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

Key Features

Space & Schema Discovery — List all your spaces (apps) and retrieve the full schema to understand your custom databases and fields.
Entity Management — Query, create, update, and delete entities across any of your custom databases flawlessly.
Comment Integration — Read and add comments to entities to keep your team in sync natively.
Advanced Querying — Use granular filters and field selections to retrieve exactly the data you need synchronously.
Cross-Database Search — Search for information across your entire workspace flawlessly through the agent.

The Fibery MCP Server exposes 11 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Fibery to CrewAI via MCP

Follow these steps to integrate the Fibery MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

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

Customize the agent

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

Run the crew

Run python crew.py. CrewAI auto-discovers 11 tools from Fibery

Why Use CrewAI with the Fibery MCP Server

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

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

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

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

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

Fibery + CrewAI Use Cases

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

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

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

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

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

Fibery MCP Tools for CrewAI (11)

These 11 tools become available when you connect Fibery to CrewAI via MCP:

add_comment

Add a comment to an entity

create_entity

Create a new entity in a specific database

delete_entity

Delete an entity

get_comments

Retrieve comments for a specific entity

get_entity

Get a specific entity by its UUID

get_schema

Retrieve the full schema of the workspace, including all databases (types) and fields

list_apps

List all Fibery apps (spaces)

list_users

List all users in the Fibery workspace

query_entities

Query entities from a specific database (type)

search_entities

Search for entities by keyword across all databases

update_entity

Update an existing entity

Example Prompts for Fibery in CrewAI

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

"List all active spaces in my Fibery account."

"Show me the tasks assigned to me in the 'Software Development' space."

"Add a comment to task UUID-123 saying 'The client approved the design'."

Troubleshooting Fibery MCP Server with CrewAI

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

MCP tools not discovered

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

Agent not using tools

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

Timeout errors

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

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.

Fibery + CrewAI FAQ

Common questions about integrating Fibery MCP Server with CrewAI.

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.

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.

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.

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.

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.