Make your software something an agent can operate.
Your API was written for a developer who read the documentation first. An agent has not read anything. Giving a model a list of endpoints and hoping is the most common reason agentic features stall in the demo.
Your API
MCP serverauth · tools · planning · guardrails
Agent platforms
An endpoint is not a tool.
There is a real gap between an API that works and an API an agent can use well, and it is not a thin one.
Authentication assumes a person. Your auth was designed for a user with a session or a developer with a key. An agent is a delegated actor working on someone's behalf, sometimes across an organisation, sometimes unattended. It needs credentials that can be scoped, attributed and revoked without breaking the human login path.
Endpoints are not tools. A REST surface exposes what the system can do. A tool surface has to express what is worth doing, in language a model can reason about, at a granularity that fits one decision. Forty endpoints usually collapse into eight good tools and thirty two that should never be exposed.
Nothing tells the model when. Documentation tells a developer how. A model needs planning structure: which tool comes first, what has to be true before a write, when to stop and ask a human, and what a partial failure means.
Errors are written for humans. A 422 with a stack trace is a dead end. An agent needs an error it can act on, which means error semantics designed as part of the tool surface rather than inherited from the framework.
What we build
The MCP server. Your existing APIs wrapped as a Model Context Protocol server, deployed in your infrastructure, with your keys.
Auth that fits a delegated actor. Token exchange, scoping per tool, per tenant isolation, attribution so every agent action traces to a principal and a purpose, and revocation that does not require a deploy.
A designed tool surface. Not a one to one mapping. We decide what to expose, at what granularity, with what names and descriptions, because those descriptions are the interface the model actually reads.
Planning structure. Tool ordering, preconditions, human in the loop checkpoints on consequential actions, and recovery paths.
Guardrails. Rate limits, destructive action confirmation, dry run modes, and an audit trail of every call.
An evaluation harness. A held out set of real tasks, scored, so you can tell whether a change to a tool description improved anything. Most teams ship this last or never; it is the thing that lets you improve.
Who this is for
Software companies whose customers are starting to ask whether the product works inside Claude or another agent platform.
Enterprises with internal APIs who want their own teams to build agents against systems of record without handing out broad credentials.
Platform businesses where an agent surface is a distribution channel, not a feature.
The outcome
Your customers get agentic use cases in the tools they already work in. You did not rebuild your product to get there, and you kept the auth model, the audit trail and the ability to say no to a specific action.
Bring us your API documentation and one workflow you wish an agent could run.
Ten working days is usually enough to have a working server against a real slice of it.