Metatext MCP Server for CrewAI 10 tools — connect in under 2 minutes
Connect your CrewAI agents to Metatext through Vinkius, pass the Edge URL in the `mcps` parameter and every Metatext tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="Metatext Specialist",
goal="Help users interact with Metatext effectively",
backstory=(
"You are an expert at leveraging Metatext 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 Metatext "
"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)
* 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 Metatext MCP Server
Connect your Metatext account to any AI agent and take full control of your NLP models and data pipelines through natural conversation.
When paired with CrewAI, Metatext becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Metatext 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
- Model Orchestration — List all trained NLP models and fetch detailed metadata and training statuses
- Real-time Inference — Programmatically run predictions, classifications, and extractions using your deployed models
- Dataset Management — Enumerate datasets and create new records for model training or evaluation
- Deployment Monitoring — List active model deployments and retrieve account usage information
- Search & Discovery — Search for specific NLP models by name to quickly access their capabilities
The Metatext 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 Metatext to CrewAI via MCP
Follow these steps to integrate the Metatext 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 Metatext
Why Use CrewAI with the Metatext MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Metatext 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
Metatext + CrewAI Use Cases
Practical scenarios where CrewAI combined with the Metatext MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries Metatext 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 Metatext, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain Metatext 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 Metatext against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
Metatext MCP Tools for CrewAI (10)
These 10 tools become available when you connect Metatext to CrewAI via MCP:
create_dataset_record
Create a new record in a dataset
get_account_info
Get account information
get_dataset_details
Get details for a specific dataset
get_model_details
Get details for a specific model
list_dataset_records
List records in a dataset
list_model_deployments
List active model deployments
list_nlp_datasets
List all datasets
list_nlp_models
List all trained NLP models
run_model_inference
Run prediction on a model
search_nlp_models
Search models by name
Example Prompts for Metatext in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with Metatext immediately.
"List all my trained NLP models in Metatext."
"Analyze this text with model ID 'mod_123': 'I love this product!'"
"Add a new record to dataset 'ds_987' with text 'Refund requested' and label 'Support'."
Troubleshooting Metatext MCP Server with CrewAI
Common issues when connecting Metatext to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
Metatext + CrewAI FAQ
Common questions about integrating Metatext MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
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?
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?
Can CrewAI agents call multiple MCP tools in parallel?
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)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect Metatext with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Metatext to CrewAI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.