Where do I find my MonkeyLearn API Key?

Log in to MonkeyLearn and go to your API Settings page. You'll find your personal API Key there.

Can I use custom models I've trained myself?

Yes! The `classify_text` and `extract_text` tools work with both pre-trained models and any custom models you have created in your account. Just provide the specific Model ID.

What is a Pipeline in MonkeyLearn?

Pipelines allow you to chain multiple processing steps together (e.g., classification followed by extraction). You can use the `list_pipelines` tool to see what's available in your account.

MonkeyLearn MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: MonkeyLearn
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to MonkeyLearn through Vinkius, pass the Edge URL in the `mcps` parameter and every MonkeyLearn 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="MonkeyLearn Specialist",
    goal="Help users interact with MonkeyLearn effectively",
    backstory=(
        "You are an expert at leveraging MonkeyLearn 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 MonkeyLearn "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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* 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 MonkeyLearn MCP Server

Connect your MonkeyLearn account to your AI agent and leverage powerful NLP models for text analysis and data extraction through natural conversation.

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

Text Classification — Use pre-trained or custom classifiers for sentiment analysis, topic detection, and intent classification.
Data Extraction — Automatically pull keywords, entities, and specific data points from raw text strings.
Model Discovery — List and inspect all classifiers, extractors, and pipelines available in your account.
Workflow Tracking — Monitor your automated workflows and processing activity in real-time.
Tag Hierarchy — Access the tag trees used by your models to understand classification structures.
Deep Inspection — Fetch detailed configuration and metadata for specific models using their unique IDs.

The MonkeyLearn MCP Server exposes 10 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 MonkeyLearn to CrewAI via MCP

Follow these steps to integrate the MonkeyLearn 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 10 tools from MonkeyLearn

Why Use CrewAI with the MonkeyLearn MCP Server

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

MonkeyLearn + CrewAI Use Cases

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

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

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

MonkeyLearn MCP Tools for CrewAI (10)

These 10 tools become available when you connect MonkeyLearn to CrewAI via MCP:

classify_text

Classify text using a model

extract_text

Extract data from text

get_classifier_details

Get classifier metadata

get_extractor_details

Get extractor metadata

list_activity

List account activity

list_classifiers

g., sentiment analysis, topic detection) available in your account. List available classifiers

list_extractors

g., keyword extraction, entity recognition) available in your account. List available extractors

list_pipelines

List MonkeyLearn pipelines

list_tag_trees

List available tag trees

list_workflows

List automated workflows

Example Prompts for MonkeyLearn in CrewAI

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

"Classify the sentiment of this review: 'The product exceeded all my expectations, truly amazing!' using model cl_oZ9GRg8P."

"List all classifiers available in my account."

"Show me my recent processing activity."

Troubleshooting MonkeyLearn MCP Server with CrewAI

Common issues when connecting MonkeyLearn 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.

MonkeyLearn + CrewAI FAQ

Common questions about integrating MonkeyLearn 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.