How do I get my DingTalk App Key and App Secret?

Log in to the [DingTalk Developer Console](https://open-dev.dingtalk.com/), create an **Internal Enterprise Application**, then navigate to **App Information** > **Credentials** to find your AppKey and AppSecret. Make sure your application has the required API permissions (User Management, Department Management, Attendance, Approval) before using these tools.

Can I send messages to external contacts or only internal employees?

This MCP server uses **Internal Enterprise Application** authentication, which means it can only send messages to users within your organization's DingTalk tenant. For external contacts or customer notifications, you would need a different DingTalk application type with extended permissions. All user IDs must belong to employees in your enterprise directory.

How does the attendance tracking work? What data can I retrieve?

The attendance tool retrieves check-in/check-out records from DingTalk's attendance system. For each record, you get the user ID, timestamp, checkout type (上班签到=check-in, 下班签退=check-out), location information, and whether the attendance was normal or abnormal (late, early leave, missing punch). Date ranges are specified in YYYY-MM-DD format. This data integrates with DingTalk's physical attendance devices and mobile check-in features.

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

Name: DingTalk
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to DingTalk through Vinkius, pass the Edge URL in the `mcps` parameter and every DingTalk 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="DingTalk Specialist",
    goal="Help users interact with DingTalk effectively",
    backstory=(
        "You are an expert at leveraging DingTalk 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 DingTalk "
        "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 DingTalk MCP Server

Connect your DingTalk (钉钉) enterprise account to any AI agent and transform your office operations through natural conversation. DingTalk is Alibaba's comprehensive B2B communication and collaboration platform used by millions of organizations for messaging, attendance tracking, approval workflows, and organizational management.

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

User Management — Query employee profiles, search users by department, and retrieve contact details instantly
Department Exploration — Navigate organizational hierarchy, list departments and sub-departments, understand reporting structures
Work Notifications — Send text and markdown formatted messages to employees with rich formatting and clickable links
Attendance Tracking — Retrieve check-in/check-out records, verify timesheet data, monitor late arrivals and early departures
Approval Workflows — Create new approval instances (leave requests, reimbursements, purchases) and track their progress
Approval Status — Query approval process history, identify bottlenecks, and review decision chains
Markdown Reports — Send beautifully formatted markdown reports, alerts, and summaries to team members

The DingTalk 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 DingTalk to CrewAI via MCP

Follow these steps to integrate the DingTalk 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 DingTalk

Why Use CrewAI with the DingTalk MCP Server

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

DingTalk + CrewAI Use Cases

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

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

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

DingTalk MCP Tools for CrewAI (10)

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

create_approval_process

g., leave request, reimbursement, purchase order) by creating a new approval instance. Requires the approval template code (process_code) from your DingTalk admin, form component values matching the template structure, and the originator's user ID. Returns the process instance ID for tracking. Use this to automate approval workflows directly from AI conversations. Create a new approval workflow instance in DingTalk

get_approval_instance

Returns whether the approval is pending, approved, rejected, or cancelled, along with all reviewer actions and timestamps. Use the process instance ID obtained when creating the approval or from the approval list. Critical for tracking approval progress and understanding bottlenecks. Get status and details of an approval process instance

get_attendance_records

Returns timestamps, checkout types (上班签到/下班签退), location data, and whether the attendance was normal or abnormal (late/early leave). Essential for HR teams to monitor attendance patterns, verify timesheet data, or investigate attendance discrepancies. Date format: YYYY-MM-DD. Get employee attendance/checkout records from DingTalk

get_department_info

Use this to understand organizational hierarchy, identify department leaders, or map the reporting structure before making decisions about notification routing. Get detailed information about a DingTalk department

get_user_info

Use the user ID (userid) which can be obtained from the department user list. Essential for looking up employee details before sending targeted notifications or checking organizational structure. Get DingTalk user profile information by user ID

list_all_departments

This is the fastest way to understand the organizational structure, identify department IDs for further queries, and map team hierarchies. Use this before querying users or sub-departments to identify the correct department IDs. List all top-level departments in the DingTalk organization

list_sub_departments

Essential for exploring organizational structure, identifying team subdivisions, or mapping the complete departmental hierarchy. Start with department_id 1 to list all top-level departments in your organization. List all sub-departments under a parent department

list_users_by_department

Returns user IDs, names, avatars, and basic profile information. Useful for identifying team members before sending group notifications, checking team composition, or understanding departmental structure. Use department ID 1 for the root company directory. List all users in a specific DingTalk department

send_markdown_message

Ideal for sending structured reports, formatted alerts, or detailed notifications with clickable links. The title appears as the notification header, while the text body supports full markdown syntax including **bold**, *italic*, [hyperlinks](url), and line breaks. User IDs should be comma-separated. Send a rich formatted markdown message to DingTalk users

send_work_notification

Supports text and markdown message types. The message appears in the recipient's DingTalk work notification feed. User IDs should be comma-separated for multiple recipients. This is ideal for sending alerts, reminders, task assignments, or status updates to team members directly through DingTalk. Send a work notification message to DingTalk users

Example Prompts for DingTalk in CrewAI

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

"List all users in department ID 12345."

"Send a markdown notification to user1,user2 with title 'Sprint Review' and content about tomorrow's meeting at 2pm."

"Check attendance records for user1,user2 from 2024-01-15 to 2024-01-19."

Troubleshooting DingTalk MCP Server with CrewAI

Common issues when connecting DingTalk 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.

DingTalk + CrewAI FAQ

Common questions about integrating DingTalk 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.