Pricefx MCP. Build complex quotes using natural language chat.
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Pricefx MCP Server gives your AI agent direct access to enterprise CPQ logic. Use it to find base SKUs, manage B2B customer records, and generate complex pricing quotes instantly via chat commands.
What your AI agents can do
Create customer
Creates a new customer record by generating and binding the necessary structured payload.
Create quote
Generates a dynamic quote structure, verifying that all complex CPQ calculations are correct.
Delete quote
Permanently deletes specific draft quotes after extracting rich validation flags.
The server lets your agent look up base SKUs in the catalog and retrieve their explicit pricing limits using get_product.
Your AI client can read existing customer data (fetch_customers), update records (update_customer), or generate entirely new B2B profiles (create_customer).
The agent builds dynamic quotes in real time using create_quote. It also checks the history of existing quotes with get_quote.
You can find out which quotes are active or stalled by running fetch_quotes, and you can permanently remove draft documents using delete_quote.
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Pricefx MCP Server: 10 Tools for Pricing Data Management
Use these tools to manage customer records, product catalogs, and complex quote validations directly through your AI agent.
019d75f9create customer
Creates a new customer record by generating and binding the necessary structured payload.
019d75f9create quote
Generates a dynamic quote structure, verifying that all complex CPQ calculations are correct.
019d75f9delete quote
Permanently deletes specific draft quotes after extracting rich validation flags.
019d75f9fetch customers
Retrieves a list of bounded CRM records stored in the Pricefx platform.
019d75f9fetch products
Lists all structured rules attached to your catalog, exporting active pricing data.
019d75f9fetch quotes
Identifies a list of active quotes that span the native Gateway authentication.
019d75f9get customer
Extracts detailed properties for an account, driving current billing and pricing logic.
019d75f9get product
Retrieves specific product details, including its explicit cloud logging and defined limits.
019d75f9get quote
Runs an automated check to pull the full history of a given quote ID.
019d75f9update customer
Updates existing customer records by accepting bulk bounds and mitigating specific plan math rules.
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 Pricefx, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ 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
What you can do with this MCP connector
Listen, your agent gets direct access to enterprise CPQ logic with this server. You don't have to mess with structured JSON payloads or clunky API calls just to build a quote. This thing lets your AI client handle core pricing tasks—from checking product rules to building final quotes—all through simple chat commands.
Product Intelligence and Catalog Checks
When you need to know what's available, the server gives you tools for deep catalog dives. To check specific products, run get_product. This pulls detailed information including its explicit cloud logging status and defined limits. If you gotta see every rule attached to your product line, use fetch_products; it lists out all structured rules in your catalog, exporting live active pricing data right away.
Managing B2B Customer Accounts
You deal with accounts constantly, so the server gives you ways to manage those records. To get deep details on an existing account—the kind of info that drives current billing and pricing logic—your agent uses get_customer. If you need a comprehensive list of all bounded CRM records in Pricefx, you run fetch_customers.
Need to spin up a brand new B2B profile? You use create_customer to build the record from scratch by generating and binding all necessary structured payloads. If an account already exists but needs tweaks, update_customer accepts bulk bounds and handles complicated plan math rules for you.
Generating and Auditing Quotes (The CPQ Flow)
This is where the magic happens. To build a dynamic quote in real time using natural language commands, your agent kicks off the process with create_quote. This tool generates the full quote structure while verifying that every complex CPQ calculation is correct. If you need to check on an existing deal's progress, run fetch_quotes for a list of active or stalled quotes tied to the native Gateway authentication.
You can also pull the complete history for any given quote ID using get_quote. And if that draft document turns out to be junk and you gotta wipe it clean, use delete_quote to permanently remove specific drafts after extracting all rich validation flags.
How Pricefx MCP Works
- 1 Subscribe to the MCP server, then provide your Pricefx Cluster, Partition Name, and active JWT Token.
- 2 Engage your AI client (Claude, Cursor, etc.) with a natural language prompt: 'Create quote for X using product Y.'
- 3 The agent automatically runs the necessary sequence of tools (
create_customer->fetch_products->create_quote) and returns the result.
The bottom line is, you talk to your AI client like a human talking to a sales rep; it handles the complex API calls underneath.
Who Is Pricefx MCP For?
Pricing Managers and Sales Engineers who get frustrated waiting for dashboards to load. If you spend time manually cross-referencing product catalogs with customer rules, this server is for you.
Uses the agent to quickly trace the math behind a specific Quote ID during a negotiation without pulling up heavy partition dashboards.
Checks live price grids or simulates pricing logic instantly by asking the AI client, rather than waiting for system reports.
Tests dynamic JSON payloads for quote generation to build headless frontends that interact with CPQ data.
What Changes When You Connect
- Stop waiting for dashboards. You can instantly check live Price Grids or simulate pricing logic by asking the agent to use
get_productandget_customer, getting immediate answers without loading heavy partition views. - Building a new quote used to require writing perfect JSON payloads. Now, just tell your AI client: 'Create a Quote for X.' The agent handles all the structure needed to call
create_quotecorrectly. - Need to clean up drafts? Instead of navigating complex menus, ask your agent to run
delete_quote. It obliterates draft quotes matching specific constraints from your partition instantly. - When you need account data, don't pull it manually. Use
fetch_customersorget_customerthrough the chat interface. You get the exact properties needed for CPQ without leaving the conversation window. - If a quote fails approval and you need to know why, running
get_quotegives you the full history trace—all in one prompt, not across multiple tabs.
Real-World Use Cases
Handling an Urgent Deal Close
A sales engineer is negotiating with a major client. They need to know if their current pricing tier qualifies for a discount. Instead of pulling up the Pricefx dashboard, they ask their agent: 'What are the base price limits for Product X?' The agent runs get_product and instantly tells them the ceiling and any hard floor rules, keeping the momentum going.
Onboarding a New Client
A pricing manager needs to set up a client profile before sending quotes. They use the agent to first check if the customer exists via fetch_customers. If not, they ask the AI to run create_customer and injects the required JSON payload automatically.
Auditing Stalled Quotes
A finance team needs to find all quotes that were started last week but never moved past 'Draft.' They prompt the agent: 'Show me all drafts from the last seven days.' The agent runs fetch_quotes, isolates the draft records, and flags them for review.
Updating a Client's Pricing Tier
The account team confirms a client's contract changed. Instead of manually updating multiple fields in the system, they ask their agent to run update_customer with the new parameters. The tool handles applying complex internal plan math and saving the changes.
The Tradeoffs
Trying to guess required JSON structure
Manually constructing a massive, nested JSON payload for create_quote while remembering all the necessary IDs and relationships.
→
Just tell your agent: 'Create a quote for Customer XYZ using Product 123.' The agent handles the structural formatting and API syntax needed to correctly call create_quote.
Using the wrong tool for data retrieval
Trying to find a product's price by searching customer records or vice versa, leading to incomplete data.
→
Always separate reads. Use fetch_products when you need catalog rules, and use get_customer only when you need account-specific properties.
Forgetting the sequence of operations
Attempting to update a customer's record using update_customer before confirming their current details via fetch_customers, risking data corruption.
→
Always verify first. Run fetch_customers or get_customer to confirm the necessary identifiers and status codes before you ask the agent to run update_customer.
When It Fits, When It Doesn't
Use this server if your core job involves generating, modifying, or auditing B2B pricing quotes (CPQ). Specifically, if you need to read product catalogs (fetch_products), manage customer credentials (get_customer/update_customer), and perform quote lifecycle management (create_quote, delete_quote)—this is it. Don't use this server if your primary goal is basic inventory tracking or managing non-pricing related assets; those require a different type of tool. If you only need to run one single query (like checking one product price), using the specific get_product tool call might be faster, but for complex workflows involving multiple steps—like 'Find customer X, see their products, and quote them'—this server is necessary.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Pricefx. 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 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Getting pricing data usually means clicking through three different dashboards.
Right now, if you need to check a product's price ceiling against an account's status, you have to jump between the Product Catalog dashboard and the CRM partition. You copy the SKU from one tab, paste it into another, and hope you didn't lose track of which version of the data you were looking at.
With this MCP server, you just ask your agent: 'What is the price ceiling for product X for customer Y?' The agent handles the required sequence—running `get_product` and then cross-referencing it with `get_customer` properties—and gives you one clean answer.
Pricefx MCP Server: Build dynamic quotes from chat.
Building a quote used to be an exercise in API scripting. You had to assemble the customer ID, product array, discount rules, and currency codes into one massive, perfectly structured JSON payload just to call `create_quote`.
Now, you simply tell your AI client: 'Generate a new quote for Acme Corp with Product Z.' The agent manages all that structural complexity behind the scenes. It's ready.
Common Questions About Pricefx MCP
How do I use create_quote to build a pricing document? +
Just ask your AI client: 'Create a new quote for customer ABC with product XYZ.' The agent automatically gathers the necessary IDs and runs create_quote, generating the full structured quote.
Can I find out why my old quote failed validation using get_quote? +
Yes. You provide the Quote ID, and the tool executes get_quote. It returns the automated validation check history, letting you see exactly what caused the failure.
What's the difference between fetch_customers and get_customer? +
Use fetch_customers when you need a list of many accounts (like 'Show me all active customers'). Use get_customer when you already know an ID and just need to pull specific properties for that single account.
How do I delete a draft quote using delete_quote? +
You ask your agent to run delete_quote. You provide the necessary constraints (like matching IDs or dates), and it irreversibly vaporizes the specified drafts from your partition.
When using `create_customer`, what format should I use to ensure the JSON payload is robust? +
You must provide a fully structured JSON payload. The system requires specific keys (like customer ID and status) to provision hard bindings correctly. Sending malformed data will fail before reaching the CPQ core.
If I need to check every product rule, how do I use `fetch_products`? +
fetch_products enumerates all attached structured rules and exports the active pricing catalog. This tool gives you a comprehensive list of SKUs available across your partition's scope.
How can I check a single product’s price limits using `get_product`? +
get_product retrieves specific cloud logging data for one item. This function tells you the exact base ceiling and floor restrictions for a particular SKU, without needing to list every related rule.
What are the best practices for using `update_customer`? +
Always verify that your incoming data matches existing array structures. The tool requires strictly formatted bounds; otherwise, it won't mitigate specific Plan Math changes and will reject the update.
Can the AI automatically assemble the correct JSON format for creating quotes? +
Yes, perfectly. By calling create_quote, you simply tell the AI: "Make a quote for customer 105 for product XYZ". It understands Pricefx schemas and writes the robust JSON mapping necessary to fulfill the API logic.
Does the server handle Pricefx partitioned clusters natively? +
Yes. Pricefx securely isolates environments based on Cluster (e.g., eu1, us1) and Partition names. Our server intrinsically limits the AI to operate strictly within the bounds you configure.
If a quote creation fails due to business rules, will the AI tell me why? +
Absolutely. If the CPU logic returns a rejection—like a discount violating a minimum margin—the AI extracts the raw API traceback identifying the specific pricing constraint that halted generation.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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