CData Connect Cloud MCP. Query any database, no matter how separate it is.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
CData Connect Cloud is a universal gateway for connecting diverse, disparate databases and APIs through explicit SQL schema mapping. It lets you list every connected data source, explore their full schemas, and run complex queries against multiple backend systems from one central point.
What your AI agents can do
Cdata create connection
Configure natively a brand new backend data source proxy utilizing CData logic
Cdata execute query
Execute native proxy query routing seamlessly into the downstream DB parsing values cleanly
Cdata get schema metadata
Evaluate the complete backend graph exposing every available interaction limit mapped natively
Get a full list of every external database or API source currently linked to the system.
Review the complete blueprint for any connected data source, seeing exactly what tables and fields are available before querying.
Execute customized SQL commands to pull specific records from deeply nested or complex backends.
See a list of every functional table within a particular connected scope or workspace.
Run a quick test to confirm if the gateway can reach and communicate with a specific external source.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
CData Connect Cloud: 8 Tools
These tools allow your agent to manage data source connections, inspect metadata, and execute structured queries against diverse backends.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using CData Connect Cloud on Vinkius019d756acdata create connection
Configure natively a brand new backend data source proxy utilizing CData logic
019d756acdata execute query
Execute native proxy query routing seamlessly into the downstream DB parsing values cleanly
019d756acdata get schema metadata
Evaluate the complete backend graph exposing every available interaction limit mapped natively
019d756acdata get table columns
Explore precise schema fields declaring explicit definitions mapping purely onto the Table boundary
019d756acdata list connections
Dumps the entire array of connected external data sources natively routed through CData
019d756acdata list tables
Unpack virtually explicit structural collections mapped securely through the backend connection
019d756acdata list workspaces
Enumerate explicitly all logical virtual Workspaces segmenting organizational data groups
019d756acdata test connection
Assess logical bounds pinging explicitly the connected proxy
Choose How to Get Started
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.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with CData Connect Cloud, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CData Connect. 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.
VINKIUS INFRASTRUCTURE
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Managed infra
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Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
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Token Compression
~60% cost reduction
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.
Data architects spend hours mapping schemas and testing endpoints.
Today, getting a full picture of your data requires opening five different tabs. You manually check if each database connection is alive, then you have to drill down through documentation to find the right table name, and finally, you copy-paste SQL syntax that might fail because the field names changed last week.
With this MCP, you tell your agent to map everything. It runs `cdata_get_schema_metadata` and instantly compiles a unified view of all data sources. You get an active blueprint showing exactly where every piece of information lives.
The cdata_list_tables tool gives you immediate visibility into your entire backend structure.
Instead of logging into three separate database consoles and running `SHOW TABLES` on each one, the agent calls `cdata_list_workspaces` first to narrow down the scope. Then it uses `cdata_list_tables`, getting an aggregated list from a single command.
This eliminates manual credential switching and repetitive setup steps. The data architect just needs to point their AI client at this MCP.
What you can do with this MCP connector
Need to pull data that lives across five different databases? This MCP acts as the single gateway. Instead of writing unique connection logic for every API endpoint or database type, it maps them all into a unified layer. You can inspect what's available—listing connections and understanding schema boundaries—before you ever write an SQL query.
It lets your agent treat multiple data sources like they’re one big pool. The system manages credentials using a zero-trust proxy; your keys never sit on disk, making it safe to connect everything without compromising security. When your AI client runs the process, it executes queries directly against native schemas and extracts raw records, simplifying complex data orchestration for analysts and engineers alike.
019d756a-cc4e-707b-a415-0c45b575390f 
How CData Connect Cloud MCP Works
- 1 First, use the tool to map out what data sources are available or establish a new connection link.
- 2 Next, inspect the full metadata of the target database using schema tools to find the precise tables and column names needed.
- 3 Finally, write and execute the specific SQL query against the defined scope to get the raw records.
The bottom line is that you gain a single point in your workflow to read data from multiple, unconnected sources without changing any underlying connection code.
Who Is CData Connect Cloud MCP For?
This MCP targets Data Engineers and API Architects who get frustrated when their job requires jumping between 10 different dashboards or writing repetitive boilerplate for every new database. It's for the person tired of spending half a day just figuring out which schema field is actually the customer ID.
Building automated pipelines that pull structured data from multiple, unrelated sources into one central location.
Designing and validating external service boundaries to ensure all connected services adhere to a uniform data access pattern.
Managing the security and connectivity rules between several different third-party systems without manual intervention.
What Changes When You Connect
- Stop guessing about schema names. Use
cdata_get_schema_metadatato see the complete blueprint of every available data source before writing a single line of SQL. - If you need to link another system, use
cdata_create_connection. It establishes the backend proxy needed so your agent can access the new data without needing manual setup files. - Need to know if the connection is live? Run
cdata_test_connectionfirst. This validates connectivity and prevents failed queries when a remote service goes down. - Want to restrict what data gets queried? Use
cdata_list_workspacesto define clear logical boundaries, ensuring your agent only sees approved data sets. - When you need specific records, running an explicit query via
cdata_execute_querypulls the exact payload needed, minimizing unnecessary data transfer and cost.
Real-World Use Cases
A Marketing Analyst needs to compare web signups with CRM contacts.
The agent first runs cdata_list_connections to confirm both the website DB and the CRM are linked. Then, it uses cdata_get_schema_metadata on both ends to find matching user IDs, allowing it to run a single query that joins data from two separate sources.
A Backend Developer needs to validate new service endpoints.
The developer uses cdata_list_connections and then runs cdata_test_connection on the new endpoint. This confirms network access before writing any code, immediately flagging if the credentials or network path are wrong.
An Integration Lead needs to audit all connected data scopes.
The agent calls cdata_list_workspaces and then loops through the results using cdata_get_table_columns. This systematically generates a report showing every table name and its underlying fields across the entire platform.
A Business Analyst needs to pull data from an old, siloed database.
The analyst uses cdata_create_connection to map the legacy DB. Once connected, they use cdata_list_tables to find the correct collection and then execute a precise query using cdata_execute_query.
The Tradeoffs
Assuming connectivity
Just calling cdata_execute_query immediately because you think the source is active.
→
Always run cdata_test_connection first. If that passes, then use cdata_list_tables to confirm the table name exists before attempting the query.
Over-relying on documentation
Reading API docs and writing a complex SQL query based only on field names.
→
Use cdata_get_schema_metadata first. This shows you exactly how the data is mapped in the live environment, preventing syntax errors.
Ignoring scopes
Running a general query that pulls everything from an entire database instance.
→
Always check cdata_list_workspaces to limit your scope. This ensures you only access data within the necessary logical boundaries and keeps results clean.
When It Fits, When It Doesn't
Use this MCP if your core problem is unifying data from multiple, distinct backends (e.g., a MySQL DB, an API endpoint, and a legacy Postgres system). It's designed for multi-source reads and schema discovery. Don't use it if you are only trying to query one source that already has dedicated native connectors—those services will be simpler. If your need is complex transactional writing (i.e., 'Update this record AND send an email'), then this MCP isn't enough; you'll need a multi-MCP chain that includes messaging tools alongside the data gateway.
Common Questions About CData Connect Cloud MCP
Can I explicitly route backend programmatic SQL queries through the native CData integration matrix? +
Yes! Utilize execute_query providing explicit logic passing straight structural limits resolving downstream implicitly.
How do I explicitly explore active table schema matrix bindings natively? +
Target explicit limit matrices completely calling list_tables bounding safely native UUID mappings retrieving exactly proxy schemas natively secure.
What orchestrates the proxy connection pings natively mapped transparently? +
Yes, native traces executing explicitly under test_connection resolve infrastructure matrix logic health verifying data clusters inherently completely mapped.
How do I securely set up a new data source using `cdata_create_connection`? +
You use this tool to configure a brand-new backend proxy. Vinkius handles credentials through a zero-trust proxy, meaning your keys are used only in transit and never stored on disk.
What is the purpose of running `cdata_list_workspaces`? +
This tool enumerates all logical workspaces segmenting organizational data groups. It helps you define the scope of your search, ensuring you target the correct data matrix before querying.
How can I see the exact fields within a table using `cdata_get_table_columns`? +
The tool returns precise schema definitions for every column mapped to a specific table. You get explicit type names and data structures, allowing you to plan your query accurately.
How do I get a full inventory of existing connections using `cdata_list_connections`? +
This tool dumps the entire array of pre-configured external data sources. It gives you an immediate, comprehensive view of all connected endpoints routed through CData.
Does the system handle API rate limiting when I use `cdata_execute_query`? +
Yes, the MCP architecture manages rate limits by proxying requests. If a limit is hit on the downstream database, your agent receives an explicit error code rather than failing silently.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.