Connect Conga to ChatGPT: Automate CPQ, CLM, and Revenue Workflows

If you want to connect Conga to ChatGPT so your AI agents can automate CPQ deal desk operations, generate CLM contracts, and manage revenue lifecycles, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's function calling capabilities and Conga's complex API surface.

If your team uses Claude, check out our guide on connecting Conga to Claude or explore our broader architectural overview on connecting Conga to AI Agents.

Giving a Large Language Model (LLM) read and write access to an enterprise platform like Conga is an engineering gauntlet. You either spend weeks building, hosting, and maintaining a custom MCP server to translate LLM JSON arguments into Conga's highly specific payload structures, or you use a managed infrastructure layer.

This guide breaks down exactly how to use Truto to generate a secure, authenticated MCP server for Conga, connect it natively to ChatGPT, and execute complex CPQ and CLM workflows using natural language.

The Engineering Reality of the Conga API

A custom MCP server is essentially a self-hosted integration proxy. While the open MCP standard provides a predictable way for models to discover tools, implementing it against Conga's API requires dealing with enterprise-grade complexity.

If you decide to build a custom MCP server for Conga, you own the entire API lifecycle. Here are the specific integration challenges that break standard CRUD assumptions when working with Conga:

Relational CPQ Hierarchies

Unlike simple REST APIs where creating a record requires a flat JSON payload, Conga's CPQ (Configure, Price, Quote) architecture is deeply nested. Generating a quote means you first create a Cart, then add Cart Items. But Cart Items aren't standalone - they are tightly coupled to Product Option Groups, Attribute Matrices, and Price Escalators. Your MCP server must guide the LLM to pass the correct parent identifiers (cart_id, parent_id) at every step, or the entire transaction fails.

Asynchronous Document Generation

When automating CLM (Contract Lifecycle Management), generating a document via template merge (create_a_conga_documents_generate) is not a synchronous operation. The API returns an async job ID. To build a robust AI tool, you either have to design your MCP server to poll the status endpoint before returning a result to the LLM, or you have to expose the polling endpoints as separate tools and rely on the LLM to orchestrate the wait loop.

Strict Lifecycle State Machines

Conga strictly enforces state transitions. You cannot simply PATCH /contracts/:id to change a contract's status from "Draft" to "Activated". You must invoke specific lifecycle endpoints like conga_clm_contracts_activate or conga_cart_finalize. If an LLM hallucinates a standard REST update for a state change, Conga will reject the request. Your MCP tool schemas must explicitly define these lifecycle operations.

Rate Limit Handling

A critical architectural note: Truto does not retry, throttle, or apply backoff on rate limit errors. When the upstream Conga API returns an HTTP 429 Too Many Requests, Truto passes that error directly to the caller. Truto normalizes the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF spec. Your application or the LLM orchestration layer is entirely responsible for implementing retry and backoff logic.

Creating the Conga MCP Server

To connect ChatGPT to Conga, you first need to scope an MCP server to a specific authenticated Conga account. Truto handles the underlying OAuth tokens and exposes a single secure URL. You can do this via the Truto dashboard or programmatically via the API.

Method 1: Via the Truto UI

1. In your Truto dashboard, navigate to Integrated Accounts and connect a new Conga instance using the OAuth flow.
2. Click into the newly created integrated account and navigate to the MCP Servers tab.
3. Click Create MCP Server.
4. Give your server a descriptive name (e.g., "Conga CPQ Production").
5. Select the desired configuration (e.g., allow read and write methods, filter by contracts or carts tags).
6. Click Create and copy the generated MCP server URL.

Method 2: Via the Truto API

If you are building an application that dynamically provisions AI agents for your users, you can generate the MCP server programmatically. You will need your Truto API token and the integrated_account_id for the specific Conga connection.

curl -X POST https://api.truto.one/integrated-account/<integrated_account_id>/mcp \
  -H "Authorization: Bearer <your_truto_api_token>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ChatGPT Conga Server",
    "config": {
      "methods": ["read", "write", "custom"],
      "tags": ["clm", "cpq", "documents"]
    }
  }'

The API returns a JSON payload containing the secure URL. This URL encapsulates both routing and authentication for this specific Conga tenant.

{
  "id": "mcp_abc123",
  "name": "ChatGPT Conga Server",
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f6..."
}

Connecting the MCP Server to ChatGPT

Once you have your Truto MCP URL, you can plug it into ChatGPT. There are two primary ways to do this depending on your environment.

Method A: Via the ChatGPT UI (Custom Connectors)

If you are using ChatGPT Pro, Plus, Business, Enterprise, or Education, you can add the server directly via the interface.

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Toggle Developer mode on (MCP support is currently behind this flag).
3. Under MCP servers / Custom connectors, click to add a new server.
4. Set the Name to "Conga Automation".
5. Paste the Truto MCP URL into the Server URL field.
6. Click Save. ChatGPT will immediately connect, perform the JSON-RPC handshake, and load the Conga tools.

Method B: Via Manual Config File (SSE Transport)

If you are running a local agent orchestration framework or configuring a headless environment, you can connect via the standard MCP SSE (Server-Sent Events) transport. Create a configuration file (e.g., mcp_config.json):

{
  "mcpServers": {
    "conga": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/a1b2c3d4e5f6..."
      ]
    }
  }
}

Conga Hero Tools for ChatGPT

Truto dynamically generates MCP tools from Conga's API schema. Instead of overwhelming ChatGPT with hundreds of endpoints, you should filter your MCP server to expose the highest-leverage operations. Here are the core hero tools for CPQ and CLM workflows.

create_a_conga_clm_contract

Creates a new contract record in Conga CLM. This initializes the contract lifecycle, setting the status to Request and assigning the record owner.

"Draft a new NDA contract for Globex Corp. Set the record type to Non-Disclosure Agreement and the start date to today."

update_a_conga_clm_contract_by_id

Updates an existing Conga CLM contract. Essential for populating custom fields, adjusting contract value, or amending dates prior to signature routing.

"Update the Globex NDA contract (ID: 8a7b6c). Change the Total Contract Value to $50,000 and extend the ContractEndDate by one year."

create_a_conga_cart

Initializes a new CPQ cart. This is the mandatory first step before configuring products, adding line items, or applying pricing rules.

"Initialize a new CPQ cart for the Wayne Enterprises Q3 renewal quote."

create_a_conga_cart_item

Adds standalone products, bundles, or options to a CPQ cart. You must provide the cart_id returned from the cart creation tool.

"Add 100 enterprise software licenses (Product ID: PROD-998) to cart ID 12345. Set the billing frequency to Annual."

create_a_conga_cart_finalize

Executes the runtime finalization of a cart. This triggers Conga's pricing engine, evaluates constraint rules, and prepares the cart for checkout or quoting.

"Finalize the configuration for cart ID 12345. Let me know if any constraint rules block the finalization."

create_a_conga_documents_generate

Triggers the document generation engine. It merges a specified Conga template with data from the platform to produce a physical file (PDF/Word).

"Generate the final proposal document for contract ID 8a7b6c using the 'Standard Enterprise MSA' template."

conga_clm_contracts_activate

Transitions a fully authored and signed contract into the Activated state, making it legally binding in the system and locking further structural edits.

"Activate the Globex NDA (ID: 8a7b6c). Use the signed PDF document ID 9d8e7f as the final attachment."

For the complete inventory of available tools, schemas, and required parameters, refer to the Conga integration page.

Workflows in Action

Once the MCP server is connected, ChatGPT can orchestrate complex, multi-step operations across Conga's CPQ and CLM modules.

Scenario 1: Automated CPQ Deal Desk

A sales rep asks ChatGPT to build out a quote for a mid-market renewal.

"Build a renewal quote for Initech. Create a new cart, add 50 standard user licenses and 1 premium support package, then finalize the cart pricing."

Execution Steps:

1. create_a_conga_cart: ChatGPT creates the empty cart container and extracts the returned cart_id.
2. create_a_conga_cart_item: It calls this tool twice. First, passing the product ID for standard licenses with a quantity of 50. Second, passing the product ID for premium support with a quantity of 1. Both use the cart_id.
3. create_a_conga_cart_finalize: ChatGPT triggers the pricing engine to calculate the totals and validate rules.

Outcome: ChatGPT replies with the finalized cart total and confirms the quote is ready for the rep to review in the Conga UI.

sequenceDiagram
  participant User
  participant ChatGPT as ChatGPT
  participant Truto as Truto MCP
  participant Conga as Conga API

  User->>ChatGPT: "Build a renewal quote for Initech..."
  ChatGPT->>Truto: call create_a_conga_cart
  Truto->>Conga: POST /carts
  Conga-->>Truto: { "id": "cart_999" }
  Truto-->>ChatGPT: Result: cart_999
  ChatGPT->>Truto: call create_a_conga_cart_item (Licenses)
  Truto->>Conga: POST /carts/cart_999/items
  Conga-->>Truto: Success
  Truto-->>ChatGPT: Result: Item added
  ChatGPT->>Truto: call create_a_conga_cart_finalize
  Truto->>Conga: POST /carts/cart_999/finalize
  Conga-->>Truto: Success
  Truto-->>ChatGPT: Result: Finalized
  ChatGPT-->>User: "The cart is built and finalized. Total: $12,500."

Scenario 2: End-to-End Contract Generation

A legal ops manager needs to draft and route a standard agreement.

"Draft a new Master Services Agreement for Cyberdyne Systems. Set the value to $250k. Once the record is created, generate the document using the Standard MSA template."

Execution Steps:

1. create_a_conga_clm_contract: ChatGPT creates the contract record, populating the Account name, RecordType ("MSA"), and TotalContractValue ($250,000).
2. create_a_conga_documents_generate: Using the newly created contract ID, ChatGPT triggers the document engine to merge the standard MSA template with the contract data.

Outcome: ChatGPT confirms the contract record is created and the async document generation job has been queued successfully.

Security and Access Control

Exposing an enterprise ERP and contracting system to an LLM requires strict governance. Truto MCP servers provide multiple layers of access control configured at server creation:

• Method Filtering (config.methods): Restrict the AI to specific operation types. Setting methods: ["read"] creates a safe, read-only agent that can query contract status but cannot modify carts or pricing.
• Tag Filtering (config.tags): Scope the server to specific domains. If you only want an agent handling document generation, set tags: ["documents", "templates"] to hide CPQ cart operations entirely.
• Require API Token Auth (config.require_api_token_auth): By default, the MCP URL acts as a bearer token. For higher security, enabling this flag forces the client to pass a valid Truto API token in the Authorization header, ensuring only authenticated internal systems can invoke the tools.
• Time-to-Live (expires_at): Create ephemeral MCP servers for temporary workflows or contractor access. Once the ISO datetime is reached, Truto automatically destroys the server and invalidates the routing token.

Next Steps

Building custom MCP schemas for Conga's deeply nested architecture is an engineering sinkhole. By using Truto, you instantly convert Conga's complex API into a secure, LLM-ready toolset.

With CPQ and CLM tools exposed natively to ChatGPT, your team can automate deal desks, scale contract generation, and remove the friction from enterprise quoting workflows.