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API to MCP Development

Turn your existing API into tools AI can use safely.

KNWN audits your API and the workflows behind it, selects the operations worth exposing, and builds a production MCP server around them. Your API remains the source of truth. The new layer gives compatible AI clients a smaller, clearer, permission-aware way to use it.

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One-off project | Client-owned code and infrastructure | No required KNWN hosting

Selective tool design | Existing business rules preserved | Client-controlled deployment

API to MCP is a product-design job, not a format conversion.

Your API describes what software can request. It may contain internal resources, low-level operations, administrative endpoints, and several steps for a task the user sees as one action.

A useful MCP surface reorganizes those capabilities around requests a person can make in ordinary language. It hides irrelevant implementation detail, combines steps where the workflow requires it, preserves the user's permissions, and makes risky actions explicit.

We do not convert every endpoint. We convert the workflows worth using.

Before we build

We check whether the API is ready to support the promised experience.

Workflow fit

Which user requests create enough value to justify a tool? Which operations belong together? Which steps should remain inside your existing product?

Authentication and identity

Can the integration act for a signed-in user? How are accounts, roles, scopes, tenants, and revocation handled?

Read and write behavior

Which operations only retrieve data? Which prepare a draft? Which commit a change? What validation and confirmation already exist?

Data contracts

Are request and response shapes stable and documented? Can internal fields be translated into inputs and results the model can use reliably?

Reliability

How do rate limits, pagination, timeouts, partial failures, duplicate requests, and asynchronous operations behave?

Environments and operations

Is there a safe test environment? What logs, audit history, and deployment controls are available?

If the API cannot safely support the intended workflow, we identify the gap before proposing the implementation. A weak backend does not become reliable because an MCP layer is placed in front of it.

Each tool should map to one recognizable job.

We ask:

  1. What would the user ask for?
  2. What information is required to complete it?
  3. Which API operations and business rules are involved?
  4. What may this user read or change?
  5. Does the tool retrieve, prepare, or commit?
  6. When must the user review or confirm?
  7. What result should the model and user receive?
  8. How should the workflow recover when a dependency fails?

Example

A user does not want to call GET /items, GET /locations, GET /availability, and POST /quote-drafts separately. The user wants to say, “Find available options near this location and prepare a quote.” The MCP surface should reflect that job while your backend still performs the authoritative checks.

The MCP layer protects the boundaries your product already depends on.

Focused schemas

Inputs use clear names, constraints, and descriptions. Results return the information needed for the next decision without leaking unnecessary internal structure.

Permission mapping

The connected identity and scopes are mapped to the account, role, and tenant checks already enforced by your systems.

Safe writes

Draft and committed actions are separated where appropriate. We add confirmation, validation, idempotency, and duplicate protection according to the risk.

Error normalization

API failures become actionable tool results. The model can ask for corrected input, tell the user what cannot be completed, or direct the workflow to an approved recovery path.

Evaluations

We test intended requests, ambiguous requests, negative requests, invalid arguments, authorization failures, rate limits, dependency failures, and complete task outcomes.

Deployment

The server is packaged for the agreed client-controlled environment with configuration, secrets guidance, monitoring hooks, and operational documentation.

Your API and product remain the source of truth.

The MCP server should not duplicate pricing, permissions, inventory, validation, or other business logic that your product already owns. It calls the authoritative services, translates the workflow into focused tools, and returns a result suitable for the model and user.

This avoids:

  • Two versions of the same business rule
  • Stale data copied into a separate AI system
  • Permission checks implemented in the wrong layer
  • Integration-specific logic that cannot be reused by your product
  • A permanent dependency on KNWN infrastructure

The API-to-MCP handover can include

  • API and workflow audit
  • Approved workflow and tool map
  • Remote MCP server
  • Input and output schemas
  • Authentication and authorization integration
  • Safe write and confirmation logic
  • Pagination, rate-limit, timeout, and error handling
  • Structured results and optional UI resources
  • Automated tests and evaluation cases
  • Deployment and environment configuration
  • Logs and operational guidance
  • Source repository, documentation, and runbook

API readiness determines more of the work than endpoint count.

Scope is shaped by:

  • Number of workflows and operations involved
  • Documentation and test-environment quality
  • Authentication, scope, role, and tenant complexity
  • Read-only versus write behavior
  • Composition across several endpoints or systems
  • Pagination, asynchronous jobs, files, webhooks, and rate limits
  • Error consistency and recovery requirements
  • Optional interface resources
  • Evaluation and deployment depth

Unless explicitly included, the engagement does not include rebuilding the entire API, replacing the product's permission system, migrating unrelated backend services, building a standalone application, operating production infrastructure indefinitely, or guaranteeing third-party approval and discovery.

Frequently asked questions

Can you automatically convert our OpenAPI specification?

An OpenAPI specification can accelerate discovery and implementation, but automatic conversion is not the product we sell. We still need to choose the useful workflows, remove overlapping operations, design model-friendly schemas, preserve permissions, control writes, normalize errors, and evaluate behavior.

Does our API need to be public?

No. Your upstream API can remain private when the approved architecture connects to it securely. For public OpenAI plugin submission, however, the submitted MCP server must be available through a stable, publicly reachable HTTPS endpoint. A public proxy can forward requests to private services, but a developer tunnel does not satisfy public submission requirements.

What if our API is incomplete?

We identify the missing operations or controls during the audit. KNWN can scope limited integration work where it is necessary for the approved workflow. Any required backend reconstruction must be identified and scoped separately.

Can one tool call multiple endpoints?

Yes, when those calls form one coherent user job and the composition preserves correctness, permissions, and useful failure behavior. We do not combine unrelated actions merely to reduce the tool count.

Can you support write actions?

Yes. Write actions require clear user identity, authorization, validation, confirmation where appropriate, idempotency, auditability, and tests for duplicate and partial-failure cases.

Who operates the finished server?

The client can operate it in client-controlled infrastructure. KNWN provides the agreed deployment configuration, documentation, tests, and handover. Ongoing support is optional and separately scoped.

Show us the workflow and the API behind it.

We will identify the useful tool surface, the security and reliability requirements, and any API gaps that must be addressed before the build.

Discuss your build