Why Postman is not enough when you want to scale

Postman automatically generates basic documentation from your collections: every request becomes an endpoint with a description, parameters, examples, and so on. This works great for a small team or a single isolated service, but as your APIs, squads, and versions grow, typical problems appear: duplicated collections, inconsistent naming, mixed environments, and documentation that is hard to navigate.

On top of that, Postman is optimized for the “design and test” workflow inside the workspace, not for governing the complete technical documentation of multiple APIs, versioning it, cross‑linking it with Markdown guides, or systematically tying it to test and compliance evidence. This is where it starts to make sense to move the collection into a platform like Capydox, which lets you treat documentation as an independent asset with its own lifecycle, formats, and review process.

Key principle: structure before you publish

If your team keeps Postman collections without a clear convention, the documentation becomes inconsistent and hard to consume. That is why, before you even think about exporting or publishing, you need to agree at least on:

A naming convention for collections and folders (by service, domain, module).
A versioning strategy (for example, Customers API v1, Customers API v2).
Environment names (local, dev, staging, prod) and variables (base_url, api_key, etc.).

Postman best‑practice guides specifically recommend grouping endpoints into logical folders, writing clear descriptions, and using environments and variables so the collection is reusable and “documentable” without duplication. When you migrate to Capydox, this structure becomes the foundation for chapters, sections, and endpoint tables, so the cleaner it is, the less work you will have later.

Recommended flow: from Postman to Capydox

We can summarize the recommended flow like this:

Renderizando diagrama…

This same pattern (export Postman collection, import it into another tool, generate navigable documentation) is used by platforms like APIMatic or EchoAPI to produce API portals, so it is a proven strategy. The difference in Capydox’s case is that you also want to link that documentation to test and security evidence, which is crucial for audits and for proving that what you documented is actually being validated in your pipelines.

Step 1: define conventions for collections and folders

Collections by domain or product

A common and recommended pattern is to create one collection per service or functional domain (for example, Billing API, Users API) and use folders to group families of endpoints (/customers, /invoices, /payments).

Postman and various documentation guides stress that “organizing and grouping requests properly into folders makes the documentation much easier to navigate.” If you import a chaotic collection into Capydox, you will get an equally chaotic documentation tree; if you keep the collection clean, Capydox can mirror that structure as clear sections and subsections.

Versioning visible in names

For maintainable documentation, versioning must be explicit:

Customers API v1
Customers API v2

Instead of relying only on the path (/v1, /v2), having the version in the collection and/or folder name helps the generated docs clearly show which endpoints belong to which version. Many tools that generate docs from Postman treat each collection as a separate “API” or “API version”, so this convention simplifies the mapping.

Step 2: enrich the collection with documentation in Postman

Before exporting, squeeze as much value as possible out of Postman’s built‑in documentation features, because all of that content will be reused in Capydox.

Descriptions for collections, folders, and requests

Postman’s official docs recommend:

Adding a general description to the collection (API purpose, base URL, authentication).
Using the folder description to explain the module or resource (for example, “Customer operations”).
Documenting each request with a short functional explanation: what it does, when it is used, what it returns.

Practical guides highlight that these descriptions are turned directly into sections and paragraphs in the generated documentation. For example, APIMatic exports collection, folder, and request descriptions as headings and text in the documentation portal. Capydox can follow a similar approach when building the base technical document.

Documenting parameters, body, and responses

Postman lets you thoroughly document query parameters, headers, and body, including types, default values, ranges, and required fields. Key recommendations:

Fill in name, type, description, and example for each parameter.
Include request and response examples (success and error) using the “Add example” feature.
Clearly indicate authentication, scopes, and common errors.

Best practices emphasize adding error examples (401, 403, 404, 422) and not just success cases, because developers and QA need to understand how the API handles problematic situations. When those examples are imported into Capydox, they can be turned into tables, JSON snippets, and “Error flows” sections without you having to rewrite anything.

Step 3: export the stable collection (v2.1)

Once you have a “clean” and functional collection, it is time to freeze a stable version and export it from Postman.

Docs and recent articles recommend using the Collection v2.1 format when exporting, because it is the current standard and the one most third‑party tools support.

Typical steps to export a Postman collection in v2.1:

Go to the Collections tab.
Click the three dots ... on the collection.
Choose Export.
Select Collection v2.1 and save the .json file.

Import/export guides also stress that v2.1 is the format that will work best with external tools that transform collections into documentation. Capydox can use that same file as the source of truth to build your initial technical document.

Step 4: import the collection into Capydox

Once you have the exported JSON, the next step is to import it into Capydox to turn it into maintainable API documentation.

Even though Capydox does not yet have as much public documentation as other platforms, its focus on documentation and security evidence makes it ideal for:

Analyzing your Postman collection and generating a documentation structure based on endpoints, methods, and descriptions.
Linking each endpoint to test evidence (for example, collections run in CI or captured results).
Keeping that documentation synchronized sprint after sprint, without destroying the content you already curated.

The idea is similar to other tools that “read” Postman Collections and build documentation portals, such as EchoAPI or APIMatic, which automatically validate and organize endpoints, parameters, and models. In your case, Capydox becomes the layer where that documentation becomes governable, versioned, and aligned with security/compliance requirements.

Step 5: review endpoints and descriptions in Capydox

After the import, it is good practice to walk through the generated endpoint tree and review:

Resource and operation names (so they match business terminology).
Descriptions inherited from Postman (to complete or rewrite them where they lack context).
Grouping into sections (for example, “Authentication”, “Customer CRUD”, “Billing Operations”).

Here Capydox adds value by allowing you to add additional Markdown context, diagrams, and usage guides around the endpoint reference, something other Postman‑based documentation platforms also promote. For example, you can add:

A specific “Getting Started” guide.
End‑to‑end use cases that chain multiple endpoints.
Security considerations and usage limits.

All of this without duplicating the technical definition of parameters, routes, and bodies, which already comes from the collection.

Step 6: publish the technical document and test evidence

With the reference baseline reviewed, the next step is to publish the documentation in whatever format your team uses:

Internal technical document (Markdown, HTML, PDF).
Internal developer portal.
Evidence repository for security audits.

Tools that turn Postman into documentation usually let you generate navigable portals, add Markdown guides, and, in some cases, link generated SDKs. Capydox can follow a similar philosophy, but with a focus on:

Making it clear which endpoints are covered by automated tests.
Attaching evidence (for example, results from collections run in CI) to endpoints or key sections.
Maintaining a “change history” per sprint.

The real effect is that your documentation stops being a static snapshot and becomes an artifact with traceability: what is being tested, how it is tested, and in which version of the API.

Step 7: update every sprint without starting over

The big advantage of using this flow is that, in every sprint, your process is incremental:

The team updates the Postman collection (new endpoints, new examples, parameter changes).
You export the new stable version in v2.1 again.
You import it into Capydox, which detects changes and updates only the technical part (endpoints, schemas, examples).
You review and adjust only the documentation sections that changed, without losing the rest of the document.

This same pattern of “import a new version of the collection and regenerate docs without losing custom content” is what similar tools recommend for long‑term documentation maintenance. The result is exactly what you were aiming for:

“You get a documentation base that can be updated every sprint without having to recreate content from scratch.”

Example of a recommended collection structure

For the generated documentation to be truly readable, it is worth following a consistent collection structure. Combining Postman’s and the community’s recommendations, you could use something like:

Collection: Billing API v1
- Description: general purpose, base URL, authentication (API key, OAuth, etc.).
Folder: Auth
- POST /auth/token – Get token.
- POST /auth/refresh – Refresh token.
Folder: Customers
- GET /customers – List customers.
- POST /customers – Create customer.
- GET /customers/{id} – Get customer details.
Folder: Invoices
- GET /invoices – List invoices.
- POST /invoices – Create invoice.

Each request with:

A clear functional description.
Documented parameters (type, description, example).
200 response examples and common error examples (401, 404, 422).

When this collection is imported into Capydox, it is straightforward to turn each folder into a chapter and each request into a reference section with reusable examples.

Example of a test/documentation snippet in Postman

Since Capydox is also oriented toward evidence, it is helpful for your Postman test scripts to be clear; many tools even reuse the test text as part of the quality narrative.

js// Tests in Postman for GET /customers
pm.test("Status 200 OK", function () {
  pm.response.to.have.status(200);
});

pm.test("Response contains list of customers", function () {
  const body = pm.response.json();
  pm.expect(body).to.be.an("array");
});

pm.test("Each customer has id and email", function () {
  const body = pm.response.json();
  body.forEach((customer) => {
    pm.expect(customer).to.have.property("id");
    pm.expect(customer).to.have.property("email");
  });
});

Good Postman guides recommend this kind of readable tests that validate minimal structure and basic behavior because they are also self‑documenting: anyone reading them understands what is expected from the endpoint. In Capydox, these tests or their results can become evidence linked to the GET /customers endpoint.

Conclusion: Postman → Capydox pipeline for scalable documentation

To summarize the approach:

Postman remains your main tool for designing, testing, and documenting at the collection level.
Capydox becomes the place where that information is turned into maintainable API documentation, versioned and backed by test evidence.
The key to making this scale is to structure the collection well (names, folders, versions, environments) before exporting it and to follow a repeatable flow each sprint.

This pattern is already used by other platforms that turn Postman Collections into documentation portals and SDKs, proving it is a robust strategy to separate the “technical source” (the collection) from the “editorial layer” (the documentation). By bringing Capydox into the picture, you also add a critical axis: traceability between what is documented and the evidence that it is actually being tested and fulfilled.

How to Document APIs with Postman at Scale