The Best Way to Give an AI Agent Tool-Calling Access to Hundreds of SaaS Apps

For a production agent that needs hundreds of apps where each customer connects with their own accounts, a tool provider is usually the best option. A tool provider connects hundreds of third-party SaaS applications and provides a way for your business to connect to them using a standard called Model Context Protocol (MCP). The integration platform manages authentication (OAuth) tokens and the API keys to connect to each SaaS application, so you don’t need to. This type of setup reduces overhead for agent development. Paragon’s ActionKit is built for this type of B2B business use case.

If you’re building an AI agent like a user assistant or chatbot, you need connectivity to every cloud application to provide accuracy and avoid hallucinations. A sales agent needs the CRM. A support agent needs the ticketing system. Almost any agent benefits from Slack, Google Drive, and Notion. Multiply that by the fact that each of your customers connects their own accounts, and you’re not adding a tool, you’re building integration infrastructure. This adds tremendous overhead to development teams, and it requires a good chunk of time to test agents to avoid bugs.

So what’s the best way to do it? This article offers guidance to help you choose the best path forward and choose the right platform.

Comparison Table for All Three Options

Here is a comparison table for your AI agent integration options. The sections below the table contain details for each option, but this comparison table gives you a quick look at the advantages and disadvantages for building in-house, using third-party MCP servers, and using integration platforms.

Option 1: Build the tools in-house with third-party APIs

The first option is to write your own code. Before you think this is an easy choice, you should know that coding for AI agents is much different than coding a standard web application. It’s also the option that will take the most management post-deployment and possible extra costs until you optimize token usage on an LLM. You manage the code, create your own schema, develop the execution logic, secure OAuth tokens and API secrets, and debug when the third-party API endpoint changes how it works. Coding your own tools gives you total control of the system, and for one or two integrations it’s a reasonable choice, especially if it’s a small application. The problem is that it doesn’t scale linearly, it scales painfully. Each integration is weeks of work up-front and permanent maintenance after deployment. Your engineering team is obligated to build and manage connectors instead of focusing on other priority development projects.

In an AI-based environment, token optimization is key to avoid unnecessary budget spend. Your tools must be optimized for each API and build queries that reduce LLM token usage. This consideration takes a unique development skillset that some developers don’t have. You could waste thousands of dollars with a trial-and-error approach, or you might waste IT budget unknowingly.

This option is best for a small, fixed set of integrations you consider core to your applications and want to control code structure and logic.

Option 2: Third-party MCP servers

Model Context Protocol (MCP) is the standard protocol for connecting AI agents to third-party cloud tools. Using MCP standardizes communication with integration platforms so that you can scale AI agents to work with hundreds of cloud platforms without supporting hundreds of proprietary schemas. Integration platforms use MCP servers to bridge communication between your AI agents and cloud applications, so your development team can build AI agents for only a single connection and scale them without building new API logic.

Using MCP servers has its disadvantages. Servers don’t consolidate third-party SaaS applications like Slack, Notion, and Google Drive. You need separate servers and separate implementations for each application. Quality and maintenance vary by server. Authentication standards are inconsistent among each server provider. Servers use different transports (stdio, SSE, Streamable HTTP) and different authentication methods, so connecting several servers is not the uniform experience the protocol promises yet. If you host your own MCP servers, it’s a large IT project that requires more time for management, security, and maintenance.

This option is best for a handful of integrations where good open-source servers are available, or you want servers for non-production like staging or development.

Option 3: A tool provider and integration platform

A tool provider gives you agent-ready connectivity across all your SaaS productivity applications. The provider handles authentication, API keys, and maintenance. This option requires the least amount of overhead for developers and doesn’t require management of infrastructure. Overhead is offloaded to the integration platform provider. Providers manage two main components:

1. Agent-optimized tool schemas: Descriptions and input are designed so that the model picks the right tool based on agent queries.

2. A tool execution layer that wraps API calls into a single process and manages authentication so the AI agent can only access data it should have permissions to work with.

This option is best for enterprise businesses with dozens of cloud-based tools that must be included in AI agent answers without much development overhead and IT costs.

ActionKit is built to solve AI agent connectivity problems

Paragon’s ActionKit gives AI agents tool-calling access to third-party SaaS apps through a single integration, without your team building or hosting the connectors or managing authentication. It exposes pre-built, LLM-optimized actions as tools your agent can call over an API or an MCP server.

• One interface, multiple integrations. ActionKit gives you hundreds of integrations and thousands of prebuilt actions using JSON-Schema tools with descriptions already optimized for tool calling. You only need a single platform instead of a separate server for each of your applications.

• Managed, multi-tenant authentication. Each of your users connects to cloud applications with their own accounts. ActionKit manages OAuth tokens and ensures that the agent only accesses data that the user has permissions to use. An agent that wants to query Github data, for example, can only access repositories where the user has permission to read data.

• SaaS tools via API and MCP. Use Paragon MCP servers for plug-and-play, or use their API when you want more control over tool logic. You can also inject custom logic, and unlike open-source MCP, one ActionKit MCP server communicates with multiple cloud applications.

• Performance for agent speed. In performance benchmark tests on a Notion task across three models, ActionKit’s tools delivered the highest benchmark scores on average and had the lowest token cost per task. ActionKit was designed for LLM optimization.

How to decide which strategy is best for your business

Ask yourself three questions:

1. How many applications do you use and what accounts connect to them? If you only have a couple of applications and use the same credentials, building in-house is fine. With several applications, each customer connecting their own accounts, it’s time to look at a third-party platform to manage them all.

2. Is integration work interrupting other development projects? If maintaining connectors and OAuth is eating sprints, that’s a signal that it’s time to off-load integration to a third-party platform.

3. Do you need consolidation and consistency? If juggling many third-party servers with inconsistent authentication logic sounds like a future headache, a single provider that manages hundreds of application tokens is the cleaner path.

Frequently asked questions

What’s the best way to give an AI agent access to many SaaS applications?

For a production agent that needs access to hundreds of applications where each customer connects using their own accounts, a tool provider (integration platform) eliminates the coding overhead. An integration provider manages authentication, rather than you building a connector to an API or hosting an MCP server in-house.

Should I build agent integrations in-house or use a provider?

Build in-house if you need a small, fixed set of integrations. Use a provider once the number of integrations grows or maintenance starts interfering with other development projects.

Can’t I just use open-source MCP servers?

You can, if you believe that you can manage maintenance and coding for in-house MCP servers. Be aware that open-source servers require one server for each SaaS application, they vary in quality, don’t have consistent authentication processes, and require your own IT to maintain security and uptime.

How does authentication work across so many apps?

A tool provider manages OAuth tokens and refresh tokens for each user, so the agent accesses data the authenticated user has permissions to access. For example, an agent polling Github data will only be able to use data based on user permissions from the OAuth token.

Do integrations work across different AI models?

Yes. Agent integration platforms work with several common models like Claude, ChatGPT, and Gemini via API or MCP servers. Connectivity from your applications uses a standardized schema, so your agents can work with common models using the same schema logic.