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Making a SaaS installable by an AI agent is not the same as exposing an API. The agent must be able to discover the product, obtain credentials, choose the right capability, send valid requests, interpret errors, and complete the entire job without a dashboard handoff. Devin D’s Waitlister postmortem shows why apparently polished surfaces fail in practice: a proxy returned 403 for llms.txt, a 961 KB documentation file exceeded an answer-time fetcher’s 60-second limit, guessed package names missed the SDK, and a soft 200 status made nonexistent documentation look real. The fix was a complete API, deterministic documentation, package aliases, typed MCP tools, correct status codes, and testing with the same fetchers agents use. One live agent then created, published, tested, unpublished, and deleted a waitlist without anyone opening the dashboard.
What does “installable by AI agents” actually require?
An installable SaaS gives an agent a complete, machine-readable route from discovery to cleanup. “There is an API” is only the starting point. The agent also needs a reliable way to find the API, authenticate, understand every operation, recover from failures, and verify that the requested result exists in the public product.
In the Waitlister postmortem, the successful run took three minutes 18 seconds to create a waitlist from an empty account. A separate planning pass produced a complete integration plan in about 90 seconds using only public surfaces. Those figures matter because they describe an end-to-end path, not a human-assisted demo: create the waitlist, generate and publish its landing page, submit a test address, verify the public signup without authentication, then unpublish and delete the work.
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The project exposed five OpenAPI endpoints, 14 typed MCP tools, a real npm SDK, four package aliases, skill.md, llms.txt, and llms-full.txt. The important lesson is that the final happy path succeeded only after the hidden assumptions in each surface were removed.
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Devin D’s 2026 postmortem is the source for the implementation details and reported timings.
Why is complete API coverage the foundation?
Partial API coverage creates a guaranteed human handoff. If an agent can create a waitlist but cannot create, update, and publish its landing page, the workflow stops at the most consequential step: making the result usable.
Map the whole job before adding endpoints
Write the workflow as a sequence of state changes rather than a list of existing routes:
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- Configure every dependent resource, such as the landing page.
- Publish or activate the result.
- Perform a public, unauthenticated verification where applicable.
- Undo the test: unpublish, archive, or delete the resources.
For each step, identify the request, required credentials, response schema, success condition, and reversible cleanup operation. A route is missing if an agent must ask a person to open the dashboard, copy a value, click Publish, or confirm that a public page works.
Make state transitions explicit
Return machine-readable identifiers and status fields after every mutating operation. An agent should not infer publication from a redirect, an empty response, or a page title. Include stable IDs, current state, and the next permissible operations in the response or documentation. This reduces speculative tool calls and gives the agent a checkpoint after each action.
Design for verification, not just creation
The Waitlister run verified a public signup without authentication before cleaning up. That is a stronger test than checking that an authenticated create request returned 200: it proves the published artifact is reachable through the same boundary an end user will use.
How should I publish an agent-readable skill.md?
Publish a short, deterministic entry point that tells an agent what to do, how to authenticate, and how to check its own work. The file should behave like an executable decision tree, not a marketing overview.
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Include both credential paths
Document the two supported authentication routes separately, with the exact header or parameter format, where a user obtains each credential, required scopes, and a minimal validation request. An agent that cannot tell whether to use an API key, bearer token, or an account-linked flow will either guess or switch tools.
Put a decision tree near the top
Start with questions an agent can answer from context: Does the user already have a token? Is the task read-only or mutating? Does the operation require a published resource? Which identifier is available? Each branch should end at a concrete endpoint or tool name. Keep optional background below the operational path.
Add a self-check
The self-check should verify authentication, capability availability, and the final user-visible result. For a waitlist workflow, that means checking the created resource, confirming its landing page is published, submitting a disposable test address, and fetching the public signup page. State what a passing response looks like and which error indicates a missing permission versus a bad request.
Use labels that survive summarization
Waitlister’s single-page guide existed but was labeled in a way that a summarizer treated as a sidebar. The content was therefore overlooked even though it was present. Give the primary guide a direct title such as “Complete API integration guide,” place it in the main document flow, and repeat the canonical URL in the index. Do not rely on navigation labels such as “resources,” “overview,” or “sidebar” to carry operational meaning.
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Why do agents need compact, deterministic documentation retrieval?
Documentation must be fetchable through real agent infrastructure, not merely through a browser or local curl command. Retrieval size, proxy behavior, timeouts, status codes, and page structure all affect what an agent can use.
Keep the index small and the full guide bounded
In this case, llms-full.txt was 961 KB. It served in about 1.3 seconds from the author’s side, yet an answer-time fetcher timed out after 60 seconds. Server response time alone therefore cannot prove agent accessibility. Split large material into focused documents, keep the index concise, and make the critical setup path available without downloading the entire site.
Test the user-agent and proxy path
The agent-facing llms.txt returned HTTP 403 because a vendor proxy forwarded bot user agents to a vendor that had AI-bot blocking enabled. The origin server was not the only component that mattered. Test from the same network path, with the user-agent strings and redirect behavior used by your target fetchers. If a proxy or security service blocks legitimate retrieval, allow the documented agent path or provide a small, cacheable endpoint that does not depend on crawler exceptions.
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Return real 404s for unknown documentation slugs
Unknown documentation slugs returned a soft 200. An agent interpreted the response as a valid page and trusted nonexistent instructions. A missing page must return HTTP 404 with a machine-readable error body; reserve 200 for an actual document. Add automated tests for malformed, expired, and case-variant slugs so a framework fallback cannot silently turn them into success.
Make each URL self-describing
Every documented URL should identify its purpose, authentication requirements, request shape, response shape, and error behavior in the same retrieval path. Agents are less likely to stitch together conflicting fragments when a compact guide contains the complete operation and links to deeper reference material only when needed.
Why did package installation fail even after publishing an SDK?
Agents often infer package names from a product name, language, or common naming convention. A single canonical npm name is not enough if plausible guesses are part of the installation path.
Publish aliases that re-export the canonical SDK
Waitlister added four aliases: waitlister-js, waitlister-sdk, waitlister-node, and @waitlister/sdk. Each installs successfully, re-exports the real SDK, and carries a deprecation notice directing users to the canonical package. This turns a name guess into a working path without maintaining four independent implementations.
Keep alias behavior intentionally boring
An alias should expose the same versioned API, preserve authentication and error semantics, and state its replacement package in the package metadata and README. Automate publication so aliases cannot drift behind the canonical package. Monitor install counts and deprecation warnings to learn which names agents actually try.
Document language and runtime assumptions
State the supported runtime, module format, required environment variables, and one minimal import example. If a package is intended for server-side use only, say so before the first code sample. Deterministic installation includes knowing what happens after npm install, not merely making that command succeed.
Should my SaaS expose OpenAPI or MCP?
OpenAPI, MCP, a CLI, and documentation solve different parts of agent integration. A robust product can expose more than one, but each surface must point to the same complete capabilities and error model.
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| Surface | Workflow completeness | Setup determinism | Capability discovery | Package or URL tolerance | Authentication and errors | Retrieval and latency | Human fallback |
|---|---|---|---|---|---|---|---|
| API with OpenAPI | Complete only when every consequential state transition is covered; Waitlister documented five endpoints. | Strong when the specification is fixed at /openapi.json and valid OpenAPI 3.0.3. |
Schema-driven operation and parameter discovery. | URL is deterministic; package naming is outside the API. | Authentication, rate limits, and error shapes can be declared centrally. | Small specification is easier to retrieve than a full documentation corpus. | Still required if a needed dashboard action has no endpoint. |
| MCP | High when tools cover the entire job; Waitlister exposed 14 typed tools. | Tool names and typed inputs reduce request construction guesses. | Natural tool listing and schemas. | Depends on the server configuration and tool registry; aliases do not solve an incorrect server URL. | Must explain server credentials and return structured failures. | Tool calls avoid large document fetches, but server startup and network policy add variables. | Not needed for covered operations; unavoidable for missing capabilities. |
| CLI | Can be complete, but every command and output must cover the workflow. | Good after installation; weaker when agents must guess the package or command name. | Help output and command schemas provide discovery. | Aliases can absorb likely package-name guesses. | Exit codes and stderr must distinguish authentication, validation, and server errors. | Local help is compact; remote calls still depend on API latency. | Required for commands that stop at dashboard-only actions. |
| Documentation files | Describe the workflow but cannot execute it alone. | Strong only with stable URLs, real status codes, and explicit credential paths. | Indexes and decision trees guide selection. | Needs slug tolerance, canonical links, and a small retrieval path. | Must state headers, scopes, rate limits, and error shapes in retrievable text. | llms-full.txt was 961 KB and timed out for one fetcher after 60 seconds. |
Documentation cannot replace an absent API or tool. |
OpenAPI is the contract for HTTP capabilities; MCP packages those capabilities as typed tools; a CLI can make repeatable workflows convenient; documentation explains how to select and combine them. None compensates for missing product operations.
Why does my AI agent switch tools after a 404?
A 404 is a capability signal, not just a transport failure. Agents use status codes to decide whether a route is wrong, a resource is absent, or another tool should be tried. Ambiguous responses cause unnecessary tool switching and invented recovery steps.
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Separate route, resource, and validation failures
- Use 404 when the endpoint or resource does not exist.
- Use 400 or 422 for a syntactically valid request with invalid fields.
- Use 401 for missing or invalid credentials and 403 for credentials that lack permission.
- Use 409 for a real state conflict, such as attempting to publish an already deleted resource.
- Use 429 for rate limiting and include a retry signal when possible.
- Use 5xx only for server-side failures, with a request ID for support and observability.
Return the same structured error shape from the API, SDK, CLI, and MCP server. Include a stable code, human-readable message, field-level details when relevant, and whether retrying can help. An agent can then correct a field, request permission, wait, or stop instead of blindly changing tools.
Why did email validation reject legitimate agent test addresses?
Automated clients often use plus-tag addresses to isolate disposable tests, such as agent+run-123@example.com. A validator that rejects the plus sign blocks a practical verification method even though the address syntax is valid for many mail systems.
Accept standards-compliant plus tags unless your product has a documented, security-based reason not to. Normalize only what the product can safely normalize, preserve the original value for auditability, and state whether duplicate detection compares the full address or a provider-specific normalized form. Test valid international and edge-case inputs separately from deliberately malformed addresses so permissiveness does not become silent data corruption.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I test what an AI fetcher actually receives?
Use the real fetcher or an equivalent agent retrieval path, not only a direct request from your laptop. Devin D summarized the lesson: “Test with the real fetchers, not with curl.” The companion line is even more precise: “Curl told me everything was fine. It was, for curl.”
Build a fetcher test matrix
- Fetch
skill.md,llms.txt,llms-full.txt, the OpenAPI URL, and representative documentation slugs through the target agent toolchain. - Record the final URL, HTTP status, redirects, response size, elapsed time, content type, and user-agent at each hop.
- Repeat through the production proxy, CDN, authentication boundary, and any vendor security service.
- Run a summarization pass and check whether the primary guide remains classified as the main document rather than a sidebar or optional resource.
- Attempt unknown slugs, expired credentials, malformed fields, rate-limit conditions, and a plus-tag test address.
- Compare what the fetcher received with what the origin server generated, then fix the first divergence.
Set explicit size and timeout budgets
Do not assume that a file served in 1.3 seconds locally will be accepted by an answer-time system. Measure the complete retrieval and parsing budget, including proxy queues, redirects, decompression, and summarization. Keep critical instructions in a compact document even if a larger archive remains available for human readers.
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Test the whole agent run
Unit tests can prove that an endpoint returns the intended schema, but only a live run proves that an agent can select it. Repeat the full create-publish-verify-cleanup sequence from a new account or isolated workspace. Waitlister reported four consecutive live runs with zero wrong guesses after the surfaces were repaired.
What implementation sequence minimizes rework?
Fix the product in dependency order: capabilities first, then contracts, then discovery, then tolerance and observability.
- Inventory the user job. Draw every state transition, including publication, public verification, and cleanup.
- Complete the API. Add routes for every consequential transition and return stable IDs, states, and structured errors.
- Freeze the contract. Publish a fixed
/openapi.jsonthat validates as OpenAPI 3.0.3 and documents authentication, rate limits, five covered endpoints, and error shapes. - Expose typed tools. Map the same operations to MCP tools with explicit input and output schemas; Waitlister’s final server had 14 tools.
- Ship the SDK and aliases. Publish the canonical npm package plus likely aliases that re-export it and announce deprecation.
- Write the agent guide. Put the decision tree, both credential paths, examples, and self-check in
skill.md. - Build documentation indexes. Keep
llms.txtcompact, bound the size ofllms-full.txt, and use stable labels and real 404s. - Exercise hostile inputs. Test guessed package names, plus-tag emails, unknown slugs, missing permissions, duplicate operations, and rate limits.
- Run the real fetcher. Verify proxy, user-agent, timeout, redirect, and summarization behavior.
- Measure repeated clean runs. Use isolated accounts and require the complete workflow to finish without a dashboard click.
What should I measure after launch?
Track failures at the boundary the agent sees, not only at the origin server. Useful signals include package-install success by package name, documentation fetch status by user-agent, response size and timeout rate, 404 versus soft-200 counts, authentication failures by method, tool-selection corrections, and completion rate for the full workflow.
Also record cleanup success. A system that creates resources but leaves test pages published has an operational defect even if its create endpoint is fast. Request IDs, stable error codes, and version identifiers let you connect an agent-visible failure to the server log without exposing sensitive credentials.
What did the Waitlister postmortem prove?
The four consecutive live runs with zero wrong guesses were the result of removing ambiguity across every surface, not of adding a single “AI” feature. The agent could discover the product, install a likely package, authenticate, call typed or schema-described operations, handle valid test input, verify a public result, and clean up. The practical standard for agent readiness is therefore simple: a new agent, starting from public instructions and an empty account, can complete the user’s entire job without a human opening the dashboard.
Frequently Asked Questions
What belongs in an AI-agent onboarding checklist for a SaaS?
Include canonical discovery URLs, both supported credential methods, a minimal authenticated request, every workflow state transition, stable IDs, structured error codes, rate-limit behavior, a public verification check, and cleanup instructions. Add a test using the same fetcher and proxy path your customers’ agents will use.
How can I stop documentation changes from breaking agents?
Version the OpenAPI document, SDK, MCP tool schemas, and agent guide together, then keep redirects from old canonical URLs. Run contract tests against saved examples and fail deployment when a status code, required field, or error code changes without a version update.
Should plus-tag email addresses be accepted for automated tests?
Usually yes, because plus tags provide a safe way to distinguish disposable runs. Reject them only for a documented product or abuse-prevention reason, and return a field-level validation error that tells the agent exactly which format is allowed.
What is a useful success criterion for an agent-ready SaaS?
Start with an empty, isolated account and require a fresh agent to discover the integration, authenticate, perform the complete user job, verify the user-visible result, and remove test data. Repeat the run through production networking and count wrong tool or URL guesses, not just HTTP 200 responses.
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