Is the integration read-only?

Yes. All tools only read from your Stoplight workspace — projects, nodes, schemas, and activity logs. No modifications or deletions are possible through this integration.

What is the workspace slug?

It's the subdomain of your Stoplight URL. For example, if your workspace is at `https://acme.stoplight.io`, the slug is `acme`. You can find it in the address bar when logged in.

What data can the agent access?

Projects, workspace members, activity logs, and all project nodes (endpoints, models, articles). Each node's full content — including OpenAPI schemas — can be retrieved via `get_node_details`.

Stoplight MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Stoplight
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Stoplight through Vinkius, pass the Edge URL in the `mcps` parameter and every Stoplight 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="Stoplight Specialist",
    goal="Help users interact with Stoplight effectively",
    backstory=(
        "You are an expert at leveraging Stoplight 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 Stoplight "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

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

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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 Stoplight MCP Server

Integrate the industry-leading API design and documentation capabilities of Stoplight into your conversational AI workflows. Empower your engineering teams to explore workspaces, evaluate OpenAPI schemas, and audit API projects natively from their conversational assistant. Securely map your AI to your Stoplight workspace, enabling the orchestration of complex documentation tasks, project navigation, and architectural reviews naturally without switching contexts or opening complex dashboards.

When paired with CrewAI, Stoplight becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Stoplight 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

Workspace Exploration — Rapidly inspect top-level organizational containers invoking list_workspaces, and track operational changes programmatically leveraging list_workspace_activity.
Project Management — Audit your API documentation repositories cataloging initiatives securely using list_projects, and retrieve full visibility metadata invoking get_project_details.
Schema & Documentation Discovery — Dive deeply into specific documentation structures retrieving files, endpoints, and models leveraging list_project_nodes, and parse their raw text safely utilizing get_node_details.
Team & Governance — Map project ownership accurately and enforce governance metrics iteratively assigning roles retrieving authorized contributors naturally via list_workspace_members.

The Stoplight MCP Server exposes 7 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 Stoplight to CrewAI via MCP

Follow these steps to integrate the Stoplight 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 7 tools from Stoplight

Why Use CrewAI with the Stoplight MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Stoplight 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

Stoplight + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Stoplight 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 Stoplight, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Stoplight 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 Stoplight against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Stoplight MCP Tools for CrewAI (7)

These 7 tools become available when you connect Stoplight to CrewAI via MCP:

get_node_details

Retrieves details for a specific documentation node

get_project_details

Retrieves details for a specific Stoplight project

list_project_nodes

Lists all documentation nodes (files, endpoints, models) within a project

list_projects

Lists all projects in a specific Stoplight workspace

list_workspace_activity

Lists recent activity logs for a Stoplight workspace

list_workspace_members

Lists all members of a Stoplight workspace

list_workspaces

Lists all accessible Stoplight workspaces

Example Prompts for Stoplight in CrewAI

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

"List my Stoplight projects and show recent workspace activity."

"Retrieve the detailed schema documentation for the processing node in our core billing API project."

"List all active members in the current workspace."

Troubleshooting Stoplight MCP Server with CrewAI

Common issues when connecting Stoplight 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.

Stoplight + CrewAI FAQ

Common questions about integrating Stoplight 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.