Source-linked API specification and agent-readiness workbench

For

API platform teams, developer-experience engineers and integration leads maintaining OpenAPI specifications

Solves

API specifications drift from code, lint and test steps live in separate tools, and agent-ready contracts are assembled by hand.

Delivers

Reviewed, source-linked API contracts and agent-ready tool definitions

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce specification drift and manual tool switching while keeping API contracts under named-owner review.

1. Generate OpenAPI specifications from descriptions or imports.
2. Suggest AI-assisted spec edits with a diff before acceptance.
3. Lint specifications for errors and style issues in the same interface.
4. Preview generated API documentation.
5. Run API calls and validate endpoints with real inputs in-tool.
6. Report API design, developer-experience, security and AI-readiness insights.
7. Process specifications and keys in the browser by default.
8. Accept a user-supplied model key and model choice.
9. Export APIs as MCP tool definitions for AI agents.
10. Allow immediate use without account creation.
11. Share specifications for collaboration and feedback.
12. Connect to an AI gateway for spec quality and safety checks.
13. Render output through an open source spec renderer.
14. Scan code repositories to map APIs, usage patterns and AI readiness.
15. Control which endpoints and fields are exposed to each audience.
16. Run hosted MCP servers with authentication and tool-level permissions.
17. Track agent and user tool calls, latency, errors and feedback.
18. Flag API drift on code change and support sync or manual approval.
19. Provide CLI access for API management.
20. Ingest OpenAPI, Postman and SDKs into an internal format.
21. Generate interactive documentation and playgrounds.
22. Produce example code and multi-step workflows in multiple languages.
23. Query and generate code through MCP editor integrations.
24. Compare the reviewed result with the recorded baseline and value assumptions.
25. Capture corrections and named-owner approval before consequential use.
26. Export a versioned reviewed API contract with source references and unresolved questions.

Everything these tools do, in one app

• OpenAPI spec generation Creates OpenAPI specifications from user descriptions or imports existing specs.Found in Cursor for your API, create-api.dev by Kong
• AI-assisted spec editing Suggests edits to API specifications using AI, with a diff shown before acceptance.Found in Cursor for your API
• Integrated linting Checks API specifications for errors and style issues within the same interface.Found in Cursor for your API
• Documentation preview Shows a preview of the API documentation generated from the specification.Found in Cursor for your API
• In-tool API testing Lets users run API calls and validate endpoints with real inputs without leaving the tool.Found in Cursor for your API, Swytchcode
• API design and DX insights Provides feedback on API design quality, developer experience, security, and AI-readiness.Found in Cursor for your API
• Browser-local processing Runs all processing in the browser with no server-side storage of specs or keys by default.Found in Cursor for your API
• Bring your own model key Allows users to supply their own API key and choose the AI model.Found in Cursor for your API
• MCP export Exports APIs in a format that makes them consumable by AI agents via Model Context Protocol.Found in Cursor for your API
• No login required Enables immediate use without account creation or setup.Found in create-api.dev by Kong
• Spec sharing Provides built-in sharing capabilities for collaboration and feedback on API designs.Found in create-api.dev by Kong
• AI Gateway integration Connects with Kong's AI Gateway to enhance spec quality and safety.Found in create-api.dev by Kong
• Open source renderer Uses an open source spec renderer for transparent and customizable output.Found in create-api.dev by Kong
• API discovery from code Scans code repositories to map out APIs, usage patterns, and AI readiness without needing a spec.Found in Elva
• Audience-specific contracts Controls which endpoints and fields are exposed to different customers, partners, or agents.Found in Elva
• Hosted MCP servers with auth Runs MCP servers with authentication and enforces permissions at the tool and resource level.Found in Elva
• Agent observability Tracks which agents and users call which tools, including latency, error rates, and agent feedback.Found in Elva
• Change review workflow Flags API drift when code changes and supports automatic sync or manual approval with notifications.Found in Elva
• CLI access Provides command-line interface access for managing APIs.Found in Elva
• Spec and SDK ingestion Accepts OpenAPI, Postman, and SDKs and maps them into an internal format for AI consumption.Found in Swytchcode
• Interactive docs and playgrounds Automatically generates interactive documentation and playgrounds for testing endpoints.Found in Swytchcode
• Code and workflow generation Produces example code and multi-step workflows in multiple languages.Found in Swytchcode
• MCP-based code queries Supports Model Context Protocol to query and generate code through editor integrations.Found in Swytchcode

What goes in, what comes out

The workflow

1. In
   Start with

   Supplied specifications, code repositories, Postman collections and SDKs

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect supplied specifications

4. 3

   Code repositories

5. 4

   Postman collections and SDKs

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked API contracts and agent-ready tool definitions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for schema validation, lint rules, arithmetic and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Browser-local processing by default; hosted MCP servers and gateway checks require explicit configuration. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Specification workspace, Source-linked review, Agent and audience console. Use a project list for APIs, a large central editor with diff view, and a right-hand panel for lint findings, test results, design insights and comments. Let users compare spec versions side by side. Display draft, changes requested and approved states. Provide a share link with comments anchored to the relevant endpoint or schema. Make the task-specific outcome reviewed, source-linked API contracts and agent-ready tool definitions visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, spec versions, code-scan history, audience policies, approval states, model-key handling, MCP server permissions, usage allowances, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned specifications, code repositories, Postman collections and SDKs. Cloud source control, CI pipelines, API gateways and documentation destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: generate OpenAPI specifications from descriptions or imports; suggest AI-assisted spec edits with a diff before acceptance; lint specifications for errors and style issues in the same interface. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will API platform teams, developer-experience engineers and integration leads maintaining OpenAPI specifications use it to solve "API specifications drift from code, lint and test steps live in separate tools, and agent-ready contracts are assembled by hand"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted specification revisions per engineering hour and drift incidents after publication.
4. Measure, then decide. Track accepted specification revisions per engineering hour and drift incidents after publication; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One approved specification format and one repository source; final contract and security decisions remain with the API owner. Implement one approved input format, a bounded representative case set and the first three task modules: generate OpenAPI specifications from descriptions or imports; suggest AI-assisted spec edits with a diff before acceptance; lint specifications for errors and style issues in the same interface. Support the remaining modules with operator review. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, source-linked API contracts and agent-ready tool definitions. Retain the explicit scope boundary: One approved specification format and one repository source; final contract and security decisions remain with the API owner.

What the build depends on. Spec upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist API and security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved specification format and one repository source; final contract and security decisions remain with the API owner.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined API package. Offer a monthly platform allowance after repeat demand. Quote complex multi-repository or hosted MCP deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked API contract and agent-ready tool definition. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce specification drift and manual tool switching while keeping API contracts under named-owner review. Demonstrate a concrete reviewed, source-linked API contract and agent-ready tool definition using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

API platform teams, developer-experience engineers and integration leads professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked API contract and agent-ready tool definition from a small authorized input set, with a transparent calculation of accepted specification revisions per engineering hour and drift incidents after publication and no promised savings.

The first 30 days

1. Week 1: interview five API platform teams, developer-experience engineers and integration leads maintaining OpenAPI specifications and inspect a recent example of API specifications drift from code, lint and test steps live in separate tools, and agent-ready contracts are assembled by hand.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted specification revisions per engineering hour and drift incidents after publication, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted specification revisions per engineering hour and drift incidents after publication. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted specification revisions per engineering hour and drift incidents after publication; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked API contracts and agent-ready tool definitions. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved specification patterns, lint rules, audience policies and review examples, together with reliable delivery for a narrow API platform niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for API platform teams, developer-experience engineers and integration leads maintaining OpenAPI specifications. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Cursor for your API, create-api.dev by Kong, Elva, Swytchcode, and manual spec editing with separate lint, test and documentation tools. Compare this product with the buyer's present method on accepted specification revisions per engineering hour and drift incidents after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, repository scanning, hosted MCP server runtime, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked API contracts and agent-ready tool definitions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, schema accuracy, security boundaries and usage permissions. API owners approve substantive contract changes and publication scope. One approved specification format and one repository source; final contract and security decisions remain with the API owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.