Zenkit MCP. Manage all your structured project data from natural conversation.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Zenkit MCP Server lets your AI agent manage structured data in workspaces, lists, and entries. Use it to list all projects, update status fields across multiple collections, or create new records—all from natural language commands.
It's a direct API connection for project-grade data management.
What your AI agents can do
Create entry
Creates a new record (entry) within any specified list, requiring field values in JSON format.
Delete entry
Removes an existing entry from a specific Zenkit list.
Get list details
Fetches the complete configuration and metadata for one particular Zenkit list.
The agent fetches a full list of all Zenkit workspaces and lists contained within them.
The agent retrieves specific metadata about one workspace, providing its structure and contents.
The agent returns a list of all entries (records) within a specified Zenkit collection or list.
The agent pulls the complete configuration and structure for a single, identified list.
The agent writes a new entry into a specified Zenkit list using provided field values.
The agent modifies specific fields on an already existing record in the collection.
The agent permanently removes a specified entry from its Zenkit list.
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Supported MCP Clients
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Zenkit MCP Server: 8 Tools for Project Management
These tools give your agent full administrative control over reading and writing every type of record in your Zenkit environment.
019d7627create entry
Creates a new record (entry) within any specified list, requiring field values in JSON format.
019d7627delete entry
Removes an existing entry from a specific Zenkit list.
019d7627get list details
Fetches the complete configuration and metadata for one particular Zenkit list.
019d7627get workspace details
Retrieves detailed information about a single, specified Zenkit workspace.
019d7627list elements
Lists all the defined fields (elements) within any given Zenkit list structure.
019d7627list entries
Retrieves a full roster of records (items) contained in a specified Zenkit collection or list.
019d7627list workspaces
Lists every workspace and the associated lists within your entire Zenkit account.
019d7627update entry
Modifies one or more fields on an existing entry in a specified list.
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Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Yo, listen up. This server connects your AI agent directly into Zenkit’s core structure. You're dealing with highly structured data across workspaces, lists, and entries; this tools let your client read and write that stuff using natural language commands.
If you need to map out where everything is, first run list_workspaces. That tool gives you a full rundown of every workspace in your account and the lists contained within 'em. If you only care about one specific area, you grab more juice by calling get_workspace_details, which returns all the metadata for that single workspace.
When it comes to structure, you start with get_list_details to pull the complete configuration for any identified list. This tells you everything about how that data is built. If you just want a checklist of what fields are available in that list—the elements—you use list_elements. These tools let your agent understand the schema before it touches the records.
To read the data, you've got two main options. You can call list_entries to get a full roster of every record contained within a specific Zenkit collection or list. Or, if you need all the setup details for that list itself, get_list_details gives you that deep dive.
Now for making changes—the real work. You can write new records into any specified list by calling create_entry; you just have to feed it the field values in JSON format. Need to tweak an existing record? Use update_entry. It lets your agent modify one or more fields on a record that's already sitting in the collection.
If that entry is garbage and needs to go, delete_entry permanently removes it from its Zenkit list.
Essentially, you use this server to manage the entire data lifecycle—from mapping out all existing workspaces with list_workspaces, checking the details of any specific spot with get_workspace_details, confirming the structure with get_list_details and list_elements, listing every item with list_entries, writing new stuff with create_entry, modifying old data using update_entry, or cleaning house totally with delete_entry.
It's a direct API connection for handling project-grade data management without touching the dashboard.
How Zenkit MCP Works
- 1 Subscribe to this server and enter your Zenkit API Key.
- 2 Your AI client connects the key, giving the agent read/write access to your data structures.
- 3 You prompt the agent with a task (e.g., 'Update the status of the Q2 budget entry to complete'). The agent uses the appropriate tool to execute the change.
The bottom line is: you talk to your AI client, and it talks to Zenkit using these tools to do the work for you.
Who Is Zenkit MCP For?
Project Managers who get stuck clicking through five different dashboards just to update a single status. Ops Engineers whose job is constant data entry and record keeping. Knowledge Workers who need to pull complex, cross-project information without opening the main application.
Uses list_workspaces and update_entry to monitor task lists across multiple projects and change status fields in bulk.
Employs create_entry or delete_entry to automate record maintenance, ensuring new data is logged immediately or old records are archived.
Uses get_list_details and list_elements to quickly understand what data structure exists in a collection before retrieving specific information.
What Changes When You Connect
- You stop navigating dashboards. Instead, you tell the agent to
list_workspacesand immediately get a map of every collection in your account. - Data consistency is maintained when you use
update_entry. You don't have to remember which field needs changing; just describe the status update, and the tool handles it. - Need to know what data fields exist? Running
list_elementsshows you the exact structure of a list without having to guess or check documentation first. - The agent can act like an administrator. You don't just read data; you use
create_entryto log new items anddelete_entrywhen records are obsolete, keeping Zenkit clean. - It centralizes your view of the project. By combining calls like
list_workspacesfollowed bylist_entries, your agent gives you a complete picture across different departmental projects.
Real-World Use Cases
Monthly Project Audit
A PM needs to know the status of 20 tasks spread across three different workspaces. They ask their agent: 'Show all entries in the Marketing, Product, and Ops lists that haven't been touched this week.' The agent runs list_workspaces first, then calls list_entries for each list, compiling a single report showing outdated items.
Onboarding New Content
An Ops team member has just finished drafting five new client profiles. Instead of manually creating them in Zenkit, they ask the agent: 'Create five new entries for clients X through Y.' The agent uses create_entry repeatedly, ensuring all fields are populated correctly and immediately logging the data.
Data Cleanup Initiative
A knowledge worker finds that a collection is filled with old, irrelevant records. They prompt: 'Delete all entries from the Asset Library list older than six months.' The agent uses list_entries to verify, and then executes delete_entry, keeping only active data.
Schema Verification
A developer needs to build a new form in Zenkit but isn't sure what fields are available. They ask the agent: 'What fields does the Social Media Calendar list have?' The agent runs list_elements and returns the precise data structure, saving hours of manual investigation.
The Tradeoffs
Trying to read a full workspace history.
A user assumes that asking 'What's in Zenkit?' will dump every piece of data they own, leading to confusion and failure when the agent only provides high-level structure.
→
Start by calling list_workspaces to see your main containers. Then, narrow it down: use get_workspace_details for a specific area, or list_entries for records in one list.
Modifying data without knowing the field name.
A user tries to update an entry by saying 'Change the status,' but doesn't know the precise field name Zenkit uses (e.g., 'Status Flag'). The tool fails because the input is ambiguous.
→
First, use list_elements on that list. This shows you all available fields and their exact names. Then, reference that name when calling update_entry.
Assuming a single API call does everything.
A user expects one magic command to list workspaces, then retrieve contents, and finally update records all at once, which the agent cannot do in one step.
→
Break it down. Use list_workspaces first. Then, use get_workspace_details on a specific result. Finally, call update_entry with the necessary IDs.
When It Fits, When It Doesn't
Use this server if your core problem involves structured data management—meaning you need to read, write, or modify records within clearly defined lists and workspaces. Think 'database views' rather than 'document storage.'
Don't use it if: 1) You are managing free text notes (use a document service instead). 2) Your needs involve complex business logic like calculating derived values across multiple systems (you need an automation platform, not just CRUD tools). 3) You only need to read data and never change it (a simple list_entries might suffice, but this server offers the full write capability).
The best practice is always: first call list_workspaces, then select a workspace, get its list details via get_list_details, and finally use list_elements to understand the schema before you try to create_entry or update_entry. This sequence prevents data errors.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zenkit. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Getting an overview of your entire project structure shouldn't require navigating five different dashboards.
Today, checking what projects exist and what lists they contain means logging into Zenkit, clicking the main dashboard, scrolling through multiple menus, and manually tracking down which workspaces are active. It's a slow process built around clicks and bookmarks.
With this MCP server, you just ask your agent to list all workspaces using `list_workspaces`. The system runs that tool and spits out a clean JSON list of every workspace name—no clicking required. You get the full map immediately.
Zenkit MCP Server: Update entries with precision.
Before, updating an item meant finding its specific record ID, opening it, manually changing the status field from 'In Progress' to 'Done,' and then hitting save. If you missed one step or used the wrong ID, the data broke.
Now, your agent handles that. You tell it: 'Update the Q2 Budget entry for John Doe to Done.' The agent runs `update_entry`, finds the correct record using its internal tools, changes only the status field, and confirms the change—all in one go.
Common Questions About Zenkit MCP
How do I list all my Zenkit workspaces using list_workspaces? +
You ask your agent to run list_workspaces. It returns a full directory of every workspace name and the lists contained inside them. This is always the first step when you need a high-level overview.
Can I update an entry without knowing the list ID? (update_entry) +
No, update_entry requires specific IDs to function correctly. If you don't know the list ID, run list_workspaces first to find the parent workspace, and then use get_workspace_details to drill down.
What is the difference between list_entries and get_list_details? +
The difference is scope. list_elements shows you what data fields are available in a list, while list_entries pulls the actual records (the items) that have been created within that list.
Does create_entry handle all field types? +
Yes. As long as you provide the necessary JSON object with correct key/value pairs, create_entry will write it to the specified Zenkit list. It assumes the structure is valid.
What authentication is needed when I use the `list_workspaces` tool? +
You need a valid Zenkit API Key. The server requires this key to authenticate your agent and verify access permissions before running any operation. This ensures that only authorized clients can list or manage your workspaces.
If I use `delete_entry`, is the data permanently removed? +
Yes, deleting an entry is irreversible within Zenkit. The tool executes a direct deletion command on the record ID you provide. Always confirm the specific list and item ID before running this function.
How do I use `list_elements` to check data structures? +
list_elements pulls all field definitions for a given list, giving you the full schema. This is crucial because it shows you exactly what types of data (text, date, number) are allowed when you plan to run create_entry or update_entry.
Does `list_entries` support large datasets and pagination? +
The tool handles large collections by supporting paginated results. Instead of retrieving everything in one go, it sends data in manageable chunks. You just need to manage the cursor or page token provided in the response.
How do I find my List ID? +
Use the list_workspaces tool to see all your workspaces and the lists they contain along with their unique IDs.
Can I filter entries in a list? +
The current list_entries tool retrieves all items. For complex filtering, you can provide field-specific values when using the create or update tools.
Is it possible to see the field definitions of a list? +
Absolutely. Use the list_elements tool with a list ID to retrieve all field names, types, and configurations.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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