How do I find my Affinda API Key?

Log in to Affinda, click on your profile avatar, and go to **Settings** > **API Keys**. You can generate a new token there. Remember to copy it immediately, as it is only shown once.

What document types are supported?

Affinda supports dozens of specialized models including Resumes, Invoices, Receipts, Passports, Driver Licenses, and Purchase Orders. Use the `list_document_types` tool to see the full list for your account.

Can I parse a document via a public URL?

Yes! The `create_document` tool accepts a `url` parameter. Simply provide the direct link to the PDF or image, and Affinda will retrieve and parse it automatically.

Affinda MCP Server for CrewAI 5 tools — connect in under 2 minutes

Name: Affinda
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 5 Tools Framework

Connect your CrewAI agents to Affinda through Vinkius, pass the Edge URL in the `mcps` parameter and every Affinda 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="Affinda Specialist",
    goal="Help users interact with Affinda effectively",
    backstory=(
        "You are an expert at leveraging Affinda 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 Affinda "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 5 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 Affinda MCP Server

Connect your Affinda account to your AI agent to unlock powerful intelligent document processing (IDP). From automatically extracting details from resumes and invoices to auditing document statuses across your workspaces, your agent handles structured data extraction through natural conversation.

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

Automated Document Parsing — Upload PDFs or images of resumes, invoices, and passports for high-accuracy JSON extraction
Workspace Oversight — List and audit documents within your specific workspaces to maintain organizational control
Extraction Model Management — List available document types (Resume, Invoice, Receipt, etc.) supported by your account
Real-time Status Tracking — Retrieve the parsing status and technical metadata for any uploaded document
Metadata Insights — Quickly identify processing errors or missing data across your document library directly from chat

The Affinda MCP Server exposes 5 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 Affinda to CrewAI via MCP

Follow these steps to integrate the Affinda 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 5 tools from Affinda

Why Use CrewAI with the Affinda MCP Server

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

Affinda + CrewAI Use Cases

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

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

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

Affinda MCP Tools for CrewAI (5)

These 5 tools become available when you connect Affinda to CrewAI via MCP:

create_document

Defaults to synchronous waiting for the output. Upload and parse a PDF or image into Affinda via its public URL for high-accuracy JSON extraction

get_document

Retrieve the fully structured JSON data and status for a specific processed document in Affinda

list_document_types

Retrieve exactly which parsing models the Affinda account supports (e.g. Resume, Invoice, Passport)

list_documents

Retrieve all parsed documents in an Affinda workspace with their processing status

list_workspaces

Retrieve all container workspaces for documents created within your Affinda account

Example Prompts for Affinda in CrewAI

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

"List all documents in my 'HR Recruitment' workspace."

"Parse this resume URL: https://example.com/cv.pdf using the 'resume' model."

"List the available document types in my account."

Troubleshooting Affinda MCP Server with CrewAI

Common issues when connecting Affinda 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.

Affinda + CrewAI FAQ

Common questions about integrating Affinda 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.