Where do I find my Mnemonic API Key?

Log in to the Mnemonic Developer Dashboard at https://dashboard.mnemonic.dev/ to generate your API key.

Which blockchains are supported?

Mnemonic currently supports Ethereum and Polygon NFT data.

Is the pricing data real-time?

Yes, Mnemonic provides live market statistics aggregated from major NFT marketplaces.

Mnemonic MCP Server for CrewAI 12 tools — connect in under 2 minutes

Name: Mnemonic
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Mnemonic through Vinkius, pass the Edge URL in the `mcps` parameter and every Mnemonic 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="Mnemonic Specialist",
    goal="Help users interact with Mnemonic effectively",
    backstory=(
        "You are an expert at leveraging Mnemonic 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 Mnemonic "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 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 Mnemonic MCP Server

Connect your Mnemonic account to your AI agent and unlock deep insights into the NFT ecosystem and Web3 data through natural conversation.

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

Portfolio Tracking — List all NFTs owned by any wallet address and view their complete historical activity.
Collection Analytics — Get real-time stats including floor price, volume, market cap, and ownership distribution.
Market Pricing — Access real-time and historical pricing data for tokens across major marketplaces.
Transfer Monitoring — Query historical transfer events for specific tokens or entire collections with advanced filters.
Token Inspection — Retrieve complete metadata, properties, and current owners for any specific NFT.
Smart Contract Data — Fetch detailed technical metadata for blockchain smart contracts and collections.

The Mnemonic MCP Server exposes 12 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 Mnemonic to CrewAI via MCP

Follow these steps to integrate the Mnemonic 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 12 tools from Mnemonic

Why Use CrewAI with the Mnemonic MCP Server

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

Mnemonic + CrewAI Use Cases

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

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

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

Mnemonic MCP Tools for CrewAI (12)

These 12 tools become available when you connect Mnemonic to CrewAI via MCP:

get_collection_details

Get NFT collection info

get_collection_distribution

Get ownership distribution

get_collection_stats

Get collection statistics

get_contract_metadata

Get smart contract metadata

get_nft_details

Get detailed NFT metadata

get_nft_owners

Get owners of an NFT

get_nft_prices

Get NFT market pricing data

get_wallet_history

Get wallet transaction history

get_wallet_nfts

Get all NFTs owned by a wallet

list_collection_tokens

List tokens in a collection

list_transfers

Query NFT transfer events

search_collections

Search for NFT collections

Example Prompts for Mnemonic in CrewAI

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

"List all NFTs owned by the wallet 0x123..."

"Search for NFT collections named 'Cool Cats'."

"What is the ownership distribution for the Azuki collection?"

Troubleshooting Mnemonic MCP Server with CrewAI

Common issues when connecting Mnemonic 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.

Mnemonic + CrewAI FAQ

Common questions about integrating Mnemonic 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.