Unlocking the Power of LLMs with OpenAPI Tool Integration
Large Language Models (LLMs) are revolutionizing the way we interact with digital systems, from conversational agents to intelligent automation. But to truly harness their capabilities, especially in enterprise and developer ecosystems, it’s essential to bridge the gap between LLMs and external systems through tools—specifically APIs. This is where OpenAPI plays a pivotal role. What is OpenAPI? OpenAPI (formerly Swagger) is an open-source specification that defines a standard, machine-readable format for describing RESTful APIs. It enables developers and automated systems to understand an API’s structure—including endpoints, request parameters, authentication methods, and response types—without relying on traditional documentation or access to source code. Its adoption spans industries such as technology, finance, and healthcare, thanks to its interoperability with a wide array of tools and frameworks. Why OpenAPI Matters for LLMs Integrating OpenAPI with LLMs enhances their ability to interact with real-world systems. Here's how: Universal Interface: OpenAPI acts as a universal bridge to RESTful APIs, making it possible for LLMs to interact with services ranging from cloud infrastructure to productivity apps. Standardized Format: The standardized schema helps LLMs accurately interpret API functionality—including expected inputs and outputs—without ambiguity. Accelerated Tool Creation: Developers can efficiently build LLM-compatible tools by parsing OpenAPI definitions directly. Seamless Integration: With broad support from API tooling ecosystems, OpenAPI enables quick embedding of LLM agents into existing workflows. Supports Tool Calling: Tool calling allows LLMs to autonomously select and invoke relevant APIs based on user prompts—a key feature unlocked by structured OpenAPI descriptions. Enabling LLM Tool Calling with SnapLogic To connect LLMs with OpenAPI-defined tools, the OpenAPI Function Generator Snap plays a crucial role. This component converts any OpenAPI spec into a tool object that LLMs can use through the Tool Calling pipeline in SnapLogic. Input Options for the Generator Snap The generator supports multiple input methods: URL: Directly fetch the OpenAPI spec from a provided URL. Text Editor: Paste the raw spec into a built-in editor. Input Document: Pass the OpenAPI string as part of an input document via expression. File Upload: Select a spec file stored in the SLDB. Output Structure The generated tool output includes: sl_tool_metadata: Metadata such as security parameters, headers, and base URLs. json_schema: A schema of the input parameters. These tools can be passed into the Tool Calling Snap, which then resolves runtime variables like headers and endpoint URLs dynamically. Developers can chain this with an HTTP Client Snap to perform real API calls based on LLM outputs. Passing Through the Tool Calling Snap When the tool is passed through the Tool Calling Snap, it dynamically processes and resolves several key components using the metadata and user input: Resolved URL: The base URL and path parameters from the OpenAPI spec are combined with user-supplied values to generate the final API endpoint. Headers: Custom headers, or content-type headers are filled in based on the OpenAPI security definitions or context provided by the LLM. This resolved output makes it simple for downstream snaps (like HTTP Client) to directly execute the API call. Action tools with HTTP Client Snap Once the Tool Calling Snap generates the resolved tool data, this output can be piped directly into an HTTP Client Snap for execution: This setup effectively turns a static OpenAPI definition into a fully dynamic and executable workflow, allowing LLMs to autonomously interact with real services. Real-World Use Cases With the right configuration, LLMs can interact with virtually any OpenAPI-compliant service. This opens up a wide range of practical applications across productivity tools, developer APIs, data services, and more. Example Use Case: Load Products from FakeStore API and Save as CSV in GitHub Gist This example shows how an LLM can orchestrate a two-step integration using OpenAPI specs and tool calling via SnapLogic: Fetch Data: Retrieve product data from FakeStore API. Transform & Upload: Format the data as CSV and post it as a public GitHub Gist using GitHub’s Gist API. Main Pipeline (download) Loop Pipeline (download, github openapi file, fake store openapi file) Prompt to LLM: “Load all products from FakeStore API and upload them as a CSV file to GitHub Gist.” Pipeline Flow Breakdown Step 1: FakeStore API Tool Call OpenAPI Tool: FakeStore API spec (loaded via URL or file). LLM Task: Recognize the available /products endpoint and trigger a GET request to retrieve the full list of products. Tool Calling Snap Output: Resolved URL to https://fakestoreapi.com/products, method GET, no authentication needed. Step 2: GitHub Gist API Tool Call OpenAPI Tool: GitHub Gist API spec, with token-based authentication defined in sl_tool_metadata. LLM Task: Use the POST /gists endpoint, and construct the request body with: description: e.g., "FakeStore Products Export" public: true files: A JSON object with one file (e.g., "products.csv": { content: "<csv data>" }) Step 3: Summarize the Result LLM Task: Extract and present key details from the final Gist API response, such as: Total number of products exported Link to the created Gist (e.g., html_url) Confirmation message for the user Final Result:
Multi Pipeline Function Generator - Simplifies Agent Worker Pipeline
This article introduces a new Snap called the “Multi Pipeline Function Generator”. The Multi Pipeline Function Generator is designed to take existing Pipelines in your SnapLogic Project and turn their configurations into function definitions for LLM-based tool calling. It achieves the following: It replaces the existing chain of function generators, therefore reduces the length of the worker pipeline. Combined with our updates to the tool calling snaps, this snap allows multiple tool calling branches to be merged into a single branch, simplifying the pipeline structure. With it, users can directly select the desired pipeline to be used as a tool from a dropdown menu. The snap will automatically retrieve the tool name, purpose, and parameters from the pipeline properties to generate a function definition in the required format. Problem Statement Currently, the complexity of the agent worker pipeline increases linearly with the number of tools it has. The image below shows a worker pipeline with three tools. It requires three function generators and has three tool calling branches to execute different tools. This becomes problematic when the number of tools is large, as the pipeline becomes very long both horizontally and vertically. Current Agent Worker Pipeline With Three Tools Solution Overview One Multi Pipeline Function Generator snap can replace multiple function generators (as long as the tool is a pipeline; it's not applicable if the tool is of another type, such as OpenAPI or APIM service). New Agent Worker Pipeline Using “Multi Pipeline Function Generator” Additionally, for each outputted tool definition, it includes the corresponding pipeline's path. This allows downstream components (the Pipeline Execute snap) to directly call the respective tool pipeline with the path, as shown below. The Multi Pipeline Function Generator snap allows users to select multiple tool pipelines at once through dropdown menus. It reads the necessary data for generating function definition from the pipeline properties. Of course, this requires that the data has been set up in the pipeline properties beforehand (will be explained later). The image below shows the settings for this snap. Snap Settings How to Use the Snap To use this snap, you need to: Fill in the necessary information for generating the function definition in the properties of your tool pipeline. The pipeline's name will become the function name The information under 'info -> purpose' will become the function description. Each key in your OpenAPI specification will be treated as a parameter, so you will ALSO need to add the expected input parameters to the list of pipeline parameters. Please note that in the current design, the pipeline parameters specified here are solely used for generating the function definition. When utilizing parameters within the pipeline, you do not need to retrieve their values using pipeline parameters. Instead, you can directly access the argument values from the input document, as determined by the model based on the function definition. Then, you can select this pipeline as a tool from the dropdown menu in the Multi Pipeline Function Generator snap. In the second output of the tool calling snap, we only need to keep one branch. In the pipeline execute snap, we can directly use the expression $sl_tool_metadata.path to dynamically retrieve the path of the tool pipeline being called. See image below. Below is an example of the pipeline properties for the tool 'CRM_insight' for your reference. Below is the settings page of the original function generator snap for comparison. As you can see, the information required is the same. The difference is that now we directly fill this information into the pipeline's properties. Step 3 - reduce the number of branches More Design Details The tool calling snap has also been updated to support $sl_tool_metadata.path , since the model's initial response doesn't include the pipeline path which is needed. After the tool calling snap receives the tools the model needs to call, it adds the sl_tool_metadata containing the pipeline path to the model's response and outputs it to the snap's second output view. This allows us to use it in the pipeline execute snap later. This feature is supported for tool calling with Amazon Bedrock, OpenAI, Azure OpenAI, and Google GenAI snap packs. The pipeline path can accept either a string or a list as input. By turning on the 'Aggregate input' mode, multiple input documents can be combined into a single function definition document for output, similar to that of a gate snap. This can be useful in scenarios like this: you use a SnapLogic list snap to enumerate all pipelines within a project, then use a filter snap to select the desired tool pipelines, and finally use the multi pipeline function generator to convert this series of pipelines into function definitions. Example Pipelines Download here. Conclusion In summary, the Multi Pipeline Function Generator snap streamlines the creation of function definitions for pipeline as tool in agent worker pipelines. This significantly reduces pipeline length in scenarios with numerous tools, and by associating pipeline information directly with the pipeline, it enhances overall manageability. Furthermore, its applicability extends across various providers.Using Mustache Templating with the Prompt Generator Snap in SnapLogic
In the world of AI-driven data integration, the ability to dynamically generate prompts is crucial for creating adaptable and responsive workflows. The Prompt Generator Snap in SnapLogic leverages Mustache templating to allow users to craft dynamic text outputs based on input data. This whitepaper aims to educate users on the fundamentals of Mustache templating and how to effectively utilize it within the Prompt Generator Snap.
