Most AI tools can tell someone how to complete a data task. Ask one to help sync data to Salesforce, and it might write the query, suggest the field mappings, and explain how to configure the sync. But someone still has to take those instructions and do the work.

Polytomic’s MCP server allows an AI agent to carry out the work itself. A user can explain the outcome they want to Claude, ChatGPT or any MCP-compatible AI agent. The AI agent can then use Polytomic to create syncs, inspect configurations, run queries, and perform other operations.

It is the difference between being told how to complete a task and having the task completed for you. A user could ask an agent to create a sync from a custom SQL query to Salesforce, sync HubSpot data into BigQuery, or run an arbitrary query against a data warehouse.

The underlying operations still run through Polytomic. Instead of configuring every step manually, the user can describe the desired result in a conversation and have the agent carry out the operation to achieve it.

Execute Polytomic Operations in Plain English

The Polytomic MCP server gives AI agents a way to communicate directly with Polytomic. Once connected with Claude, ChatGPT, or another MCP-compatible AI agent, users can execute Polytomic operations by describing what they want in plain English.

Without this connection, an AI agent can explain how to use Polytomic, draft a query, or provide a list of steps. The user still has to take that guidance into Polytomic and complete the operation. With the MCP server, the AI agent no longer stops at explaining the next steps. It can use Polytomic to carry them out.

The work still runs through the connections and permissions already established in Polytomic. Instead of configuring each part of the operation manually, the user can describe the desired result in plain English and have the agent carry it out through Polytomic.

Create Data Syncs From a Conversation

Setting up a data sync involves a series of configuration choices. The user must define the source data, select the relevant records and fields, configure the destination, and map the fields. The sync may also require matching rules, filters, and a schedule.

The Polytomic MCP server lets users make these choices through a conversation. A user can describe what data should move, where it should go, and how the sync should behave. The agent can then use the connections already available in Polytomic to create the corresponding data sync.

This works for Model Syncs (moving selected or modeled data into business applications) and Bulk Syncs (replicating source data into databases, data warehouses, or cloud storage). The following examples show how an agent can configure each type from a conversation.

Example: Generate a SQL Query in Snowflake and Sync the Results to Salesforce

Suppose a company wants to calculate a customer health score using data in Snowflake and make that score available to its sales team in Salesforce. To turn those criteria into a recurring sync, someone would have to open Polytomic, create a SQL model using the company’s Snowflake connection, select Salesforce as the destination, configure the mappings and matching rules, and set the schedule.

After providing the customer health criteria in Claude or ChatGPT, the user can ask the agent to generate the query and create the sync:

The agent uses those instructions to generate the SQL query, create the SQL model, and configure the Model Sync through Polytomic. The user still has to define the business logic, but the AI agent handles the query generation and setup in Polytomic.

Example: Sync HubSpot to BigQuery

Consider a company that has to replicate its HubSpot contacts, companies, and deals into a BigQuery dataset called hubspot_data for reporting and analysis. Someone would need to open Polytomic, create a Bulk Sync, select the HubSpot objects and fields, choose the destination dataset in BigQuery, and set the sync schedule.

With the Polytomic MCP server, the user can give those instructions directly to Claude or ChatGPT:

The agent uses these instructions to configure the Bulk Sync through Polytomic. The user still has to decide what data should move, where it should go, and how often the sync should run, while the agent handles the setup in Polytomic.

Query Data Without Leaving the Conversation

Not every data request requires a recurring sync. Sometimes, a user simply needs to answer a question using current data from a database or data warehouse.

An AI agent can generate a SQL query, but it cannot run the query or return the results without access to the relevant systems. Through the Polytomic MCP server, the agent can use Query Runner to generate and execute arbitrary SQL queries against databases and data warehouses already connected to Polytomic.

The user can describe the information they need. The AI agent can then generate the SQL, run the query through Polytomic Query Runner, and return the results in the same conversation.

Example: Answer an Ad Hoc Question With Snowflake Data

Suppose a product team wants to understand how customers are using a recently released feature. The relevant account, subscription, and product-usage data already exists in Snowflake, but answering the question requires someone to write and run a SQL query.

Connecting the Polytomic MCP server to Claude or ChatGPT allows the user to ask the question directly:

The agent can translate the request into SQL, run the query against Snowflake through the Polytomic Query Runner, and return the results in the conversation. The user does not need to open Query Runner in Polytomic and write a SQL query.
If the initial answer raises follow-up questions, the user can ask the agent to adjust the analysis and run another query in the same conversation.

Polytomic Becomes an Operational Layer for AI Agents

The Polytomic MCP server provides a new way to use the platform. The important shift is from manually operating Polytomic to directing it through an AI agent. Users can describe the result they want, then let the agent carry out the work through Polytomic. The underlying connections and capabilities remain the same, but users can access them from Claude, ChatGPT, or another MCP-compatible AI agent.

The Polytomic MCP server gives AI agents access to the systems and data available through a company’s Polytomic environment. Instead of setting up a separate MCP connection for each supported service, teams can use Polytomic as a single access point.

With the Polytomic MCP server, users can:

• Configure and Manage Data Syncs: Create and modify data syncs through natural-language instructions instead of configuring every workflow manually
• Query Databases and Data Warehouses: Generate queries, run them, and view results directly in the AI agent’s interface
• Access Connected Business Systems: Work with applications connected to Polytomic across sales, marketing, support, finance, and more

Book a demo to see how the Polytomic MCP server can help streamline your organization’s operations, or get started today.