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Implementing Dynamic Types in Docusign Extension Apps

Implementing Dynamic Types in Docusign Extension Apps

Implementing Dynamic Types in Docusign Extension Apps

In our previous blog post about Docusign Extension Apps, Advanced Authentication and Onboarding Workflows with Docusign Extension Apps, we touched on how you can extend the OAuth 2 flow to build a more powerful onboarding flow for your Extension Apps. In this blog post, we will continue explaining more advanced patterns in developing Extension Apps. For that reason, we assume at least basic familiarity with how Extension Apps work and ideally some experience developing them.

To give a brief recap, Docusign Extension Apps are a powerful way to embed custom logic into Docusign agreement workflows. These apps are lightweight services, typically cloud-hosted, that integrate at specific workflow extension points to perform custom actions, such as data validation, participant input collection, or interaction with third-party services. Each Extension App is configured using a manifest file. This manifest defines metadata such as the app's author, support links, and the list of extension points it uses (these are the locations in the workflow where your app's logic will be executed).

The extension points that are relevant for us in the context of this blog post are GetTypeNames and GetTypeDefinitions. These are used by Docusign to retrieve the types supported by the Extension App and their definitions, and to show them in the Maestro UI.

In most apps, these types are static and rarely change. However, they don't have to be. They can also be dynamic and change based on certain configurations in the target system that the Extension App is integrating with, or based on the user role assigned to the Maestro administrator on the target system.

Static vs. Dynamic Types

To explain the difference between static and dynamic types, we'll use the example from our previous blog post, where we integrated with an imaginary task management system called TaskVibe. In the example, our Extension App enabled agreement workflows to communicate with TaskVibe, allowing tasks to be read, created, and updated.

Our first approach to implementing the GetTypeNames and GetTypeDefinitions endpoints for the TaskVibe Extension App might look like the following. The GetTypeNames endpoint returns a single record named task:

{
    "typeNames": [
        {
            "typeName": "task",
            "label": "Task",
            "description": "A task on TaskVibe."
        }
    ]
}

Given the type name task, the GetTypeDefinitions endpoint would return the following definition for that type:

{
    "declarations": [
        {
            // ...
            "name": "task",
            "isAbstract": false,
            "identified": {
                "$class": "concerto.metamodel@1.0.0.IdentifiedBy",
                "name": "recordId"
            },
            "properties": [
                {
                  // ...
                  "name": "recordId"
                },
                {
                  // ...
                  "name": "title"
                },
                // Other task properties
            ]
        }
    ]
}

As noted in the Docusign documentation, this endpoint must return a Concerto schema representing the type. For clarity, we've omitted most of the Concerto-specific properties. The above declaration states that we have a task type, and this type has properties that correspond to task fields in TaskVibe, such as record ID, title, description, assignee, and so on.

The type definition and its properties, as described above, are static and they never change. A TaskVibe task will always have the same properties, and these are essentially set in stone.

Now, imagine a scenario where TaskVibe supports custom properties that are also project-dependent. One project in TaskVibe might follow a typical agile workflow with sprints, and the project manager might want a "Sprint" field in every task within that project. Another project might use a Kanban workflow, where the project manager wants a status field with values like "Backlog," "ToDo," and so on. With static types, we would need to return every possible field from any project as part of the GetTypeDefinitions response, and this introduces new challenges. For example, we might be dealing with hundreds of custom field types, and showing them in the Maestro UI might be too overwhelming for the Maestro administrator. Or we might be returning fields that are simply not usable by the Maestro administrator because they relate to projects the administrator doesn't have access to in TaskVibe.

With dynamic types, however, we can support this level of customization.

Implementing Dynamic Types

When Docusign sends a request to the GetTypeNames endpoint and the types are dynamic, the Extension App has a bit more work than before.

As we've mentioned earlier, we can no longer return a generic task type. Instead, we need to look into each of the TaskVibe projects the user has access to, and return the tasks as they are represented under each project, with all the custom fields. (Determining access can usually be done by making a query to a user information endpoint on the target system using the same OAuth 2 token used for other calls.)

Once we find the task definitions on TaskVibe, we then need to return them in the response of GetTypeNames, where each type corresponds to a task for the given project. This is a big difference from static types, where we would only return a single, generic task.

For example:

{
    "typeNames": [
        {
            "typeName": "task_project1",
            "label": "Task - Project 1",
            "description": "A task on TaskVibe, project 1."
        },
        {
            "typeName": "task_project2",
            "label": "Task - Project 2",
            "description": "A task on TaskVibe, project 2."
        }      
    ]
}

The key point here is that we are now returning one type per task in a TaskVibe project. You can think of this as having a separate class for each type of task, in object-oriented lingo. The type name can be any string you choose, but it needs to be unique in the list, and it needs to contain the minimum information necessary to be able to distinguish it from other task definitions in the list. In our case, we've decided to form the ID by concatenating the string "task_" with the ID of the project on TaskVibe.

The implementation of the GetTypeDefinitions endpoint needs to:

  1. Extract the project ID from the requested type name.
  2. Using the project ID, retrieve the task definition from TaskVibe for that project. This definition specifies which fields are present on the project's tasks, including all custom fields.
  3. Once the fields are retrieved, map them to the properties of the Concerto schema.

The resulting JSON could look like this (again, many of the Concerto properties have been omitted for clarity):

{
    "declarations": [
        {
            // ... 
            "name": "task_project1",
            "isAbstract": false,
            "identified": {
                "$class": "concerto.metamodel@1.0.0.IdentifiedBy",
                "name": "project1_task_recordId"
            },
            "properties": [
                {
                  // ...
                  "name": "project1_task_recordId"
                },
                {
                  // ...
                  "name": "project1_task_title"
                },
                {
                  // ...
                  "name": "project1_task_spring" // This is a custom property on TaskVibe!
                },
                // Other task properties
            ]
        }
    ]
}

Now, type definitions are fully dynamic and project-dependent.

Caching of Type Definitions on Docusign

Docusign maintains a cache of type definitions after an initial connection. This means that changes made to your integration (particularly when using dynamic types) might not be immediately visible in the Maestro UI. To ensure users see the latest data, it's useful to inform them that they may need to refresh their Docusign connection in the App Center UI if new fields are added to their integrated system (like TaskVibe). As an example, a newly added custom field on a TaskVibe project wouldn't be reflected until this refresh occurs.

Conclusion

In this blog post, we've explored how to leverage dynamic types within Docusign Extension Apps to create more flexible integrations with external systems. While static types offer simplicity, they can be constraining when working with external systems that offer a high level of customization. We hope that this blog post provides you with some ideas on how you can tackle similar problems in your Extension Apps.

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Largely created by Devon Govett, it is designed as a zero-configuration build tool that supports bundling, minification, tree-shaking, transpiling, compiling, HMR, and a development server. It can utilize all the necessary types of assets you will need, from JavaScript to HTML, CSS, and images. The core part of it is mostly written in JavaScript, with a CSS transformer written in Rust, whereas it delegates the JavaScript compilation to a SWC. Likewise, it also has a large collection of community-maintained plugins. Overall, it is a good tool for quick development without requiring extensive configuration. Rolldown Rolldown is the future bundler for Vite, written in Rust and built on top of Oxc, currently leveraging its parser and resolver. Inspired by Rollup (hence the name), it will provide Rollup-compatible APIs and plugin interface, but it will be more similar to esbuild in scope. Currently, it is still in heavy development and it is not ready for production, but we should definitely be hearing more about this bundler in 2025 and beyond. Rollup Rollup is the current bundler for Vite. Originally created by Rich Harris, the creator of Svelte, Rollup is slowly becoming a veteran (speaking in JavaScript years) compared to other build tools here. When it originally launched, it introduced novel ideas focused on ES modules and tree-shaking, at the time when Webpack as its competitor was becoming too complex due to its extensive feature set - Rollup promised a simpler way with a straightforward configuration process that is easy to understand. Rolldown, mentioned previously, is hoped to become a replacement for Rollup at some point. Rsbuild Rsbuild is a high-performance build tool written in Rust and built on top of Rspack. Feature-wise, it has many similiarities with Vite. Both Rsbuild and Rspack are sponsored by the Web Infrastructure Team at ByteDance, which is a division of ByteDance, the parent company of TikTok. Rsbuild is built as a high-level tool on top of Rspack that has many additional features that Rspack itself doesn't provide, such as a better development server, image compression, and type checking. Rspack Rspack, as the name suggests, is a Rust-based alternative to Webpack. It offers a Webpack-compatible API, which is helpful if you are familiar with setting up Webpack configurations. However, if you are not, it might have a steep learning curve. To address this, the same team that built Rspack also developed Rsbuild, which helps you achieve a lot with out-of-the-box configuration. Under the hood, Rspack uses SWC for compiling and transpiling. Feature-wise, it’s quite robust. It includes built-in support for TypeScript, JSX, Sass, Less, CSS modules, Wasm, and more, as well as features like module federation, PostCSS, Lightning CSS, and others. Snowpack Snowpack was created around the same time as Vite, with both aiming to address similar needs in modern web development. Their primary focus was on faster build times and leveraging ES modules. Both Snowpack and Vite introduced a novel idea at the time: instead of bundling files while running a local development server, like traditional bundlers, they served the app unbundled. Each file was built only once and then cached indefinitely. When a file changed, only that specific file was rebuilt. For production builds, Snowpack relied on external bundlers such as Webpack, Rollup, or esbuild. Unfortunately, Snowpack is a tool you’re likely to hear less and less about in the future. It is no longer actively developed, and Vite has become the recommended alternative. SWC SWC, which stands for Speedy Web Compiler, can be used for both compilation and bundling (with the help of SWCpack), although compilation is its primary feature. And it really is speedy, thanks to being written in Rust, as are many other tools on this list. Primarily advertised as an alternative to Babel, its SWC is roughly 20x faster than Babel on a single thread. SWC compiles TypeScript to JavaScript, JSX to JavaScript, and more. It is used by tools such as Parcel and Rspack and by frameworks such as Next.js, which are used for transpiling and minification. SWCpack is the bundling part of SWC. However, active development within the SWC ecosystem is not currently a priority. The main author of SWC now works for Turbopack by Vercel, and the documentation states that SWCpack is presently not in active development. Terser Terser has the smallest scope compared to other tools from this list, but considering that it's used in many of those tools, it's worth separating it into its own section. Terser's primary role is minification. It is the successor to the older UglifyJS, but with better performance and ES6+ support. Vite Vite is a somewhat of a special beast. It's primarily a development server, but calling it just that would be an understatement, as it combines the features of a fast development server with modern build capabilities. Vite shines in different ways depending on how it's used. During development, it provides a fast server that doesn't bundle code like traditional bundlers (e.g., Webpack). Instead, it uses native ES modules, serving them directly to the browser. Since the code isn't bundled, Vite also delivers fast HMR, so any updates you make are nearly instant. Vite uses two bundlers under the hood. During development, it uses esbuild, which also allows it to act as a TypeScript transpiler. For each file you work on, it creates a file for the browser, allowing an easy separation between files which helps HMR. For production, it uses Rollup, which generates a single file for the browser. However, Rollup is not as fast as esbuild, so production builds can be a bit slower than you might expect. (This is why Rollup is being rewritten in Rust as Rolldown. Once complete, you'll have the same bundler for both development and production.) Traditionally, Vite has been used for client-side apps, but with the new Environment API released in Vite 6.0, it bridges the gap between client-side and server-rendered apps. Turbopack Turbopack is a bundler, written in Rust by the creators of webpack and Next.js at Vercel. The idea behind Turbopack was to do a complete rewrite of Webpack from scratch and try to keep a Webpack compatible API as much as possible. This is not an easy feat, and this task is still not over. The enormous popularity of Next.js is also helping Turbopack gain traction in the developer community. Right now, Turbopack is being used as an opt-in feature in Next.js's dev server. Production builds are not yet supported but are planned for future releases. Webpack And finally we arrive at Webpack, the legend among bundlers which has had a dominant position as the primary bundler for a long time. Despite the fact that there are so many alternatives to Webpack now (as we've seen in this blog post), it is still widely used, and some modern frameworks such as Next.js still have it as a default bundler. Initially released back in 2012, its development is still going strong. Its primary features are bundling, code splitting, and HMR, but other features are available as well thanks to its popular plugin system. Configuring Webpack has traditionally been challenging, and since it's written in JavaScript rather than a lower-level language like Rust, its performance lags behind compared to newer tools. As a result, many developers are gradually moving away from it. Conclusion With so many build tools in today's JavaScript ecosystem, many of which are similarly named, it's easy to get lost. Hopefully, this blog post was a useful overview of the tools that are most likely to continue being relevant in 2025. Although, with the speed of development, it may as well be that we will be seeing a completely different picture in 2026!...

The simplicity of deploying an MCP server on Vercel cover image

The simplicity of deploying an MCP server on Vercel

The current Model Context Protocol (MCP) spec is shifting developers toward lightweight, stateless servers that serve as tool providers for LLM agents. These MCP servers communicate over HTTP, with OAuth handled clientside. Vercel’s infrastructure makes it easy to iterate quickly and ship agentic AI tools without overhead. Example of Lightweight MCP Server Design At This Dot Labs, we built an MCP server that leverages the DocuSign Navigator API. The tools, like `get_agreements`, make a request to the DocuSign API to fetch data and then respond in an LLM-friendly way. ` Before the MCP can request anything, it needs to guide the client on how to kick off OAuth. This involves providing some MCP spec metadata API endpoints that include necessary information about where to obtain authorization tokens and what resources it can access. By understanding these details, the client can seamlessly initiate the OAuth process, ensuring secure and efficient data access. The Oauth flow begins when the user's LLM client makes a request without a valid auth token. In this case they’ll get a 401 response from our server with a WWW-Authenticate header, and then the client will leverage the metadata we exposed to discover the authorization server. Next, the OAuth flow kicks off directly with Docusign as directed by the metadata. Once the client has the token, it passes it in the Authorization header for tool requests to the API. ` This minimal set of API routes enables me to fetch Docusign Navigator data using natural language in my agent chat interface. Deployment Options I deployed this MCP server two different ways: as a Fastify backend and then by Vercel functions. Seeing how simple my Fastify MCP server was, and not really having a plan for deployment yet, I was eager to rewrite it for Vercel. The case for Vercel: * My own familiarity with Next.js API deployment * Fit for architecture * The extremely simple deployment process * Deploy previews (the eternal Vercel customer conversion feature, IMO) Previews of unfamiliar territory Did you know that the MCP spec doesn’t “just work” for use as ChatGPT tooling? Neither did I, and I had to experiment to prove out requirements that I was unfamiliar with. Part of moving fast for me was just deploying Vercel previews right out of the CLI so I could test my API as a Connector in ChatGPT. This was a great workflow for me, and invaluable for the team in code review. Stuff I’m Not Worried About Vercel’s mcp-handler package made setup effortless by abstracting away some of the complexity of implementing the MCP server. It gives you a drop-in way to define tools, setup https-streaming, and handle Oauth. By building on Vercel’s ecosystem, I can focus entirely on shipping my product without worrying about deployment, scaling, or server management. Everything just works. ` A Brief Case for MCP on Next.js Building an API without Next.js on Vercel is straightforward. Though, I’d be happy deploying this as a Next.js app, with the frontend features serving as the documentation, or the tools being a part of your website's agentic capabilities. Overall, this lowers the barrier to building any MCP you want for yourself, and I think that’s cool. Conclusion I'll avoid quoting Vercel documentation in this post. AI tooling is a critical component of this natural language UI, and we just want to ship. I declare Vercel is excellent for stateless MCP servers served over http....

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