An agent edits the wrong config file, then spends forty minutes trying to repair its own repair. The user files a bug with a screenshot of the last message. The maintainer asks for logs, and the logs live in four places: the provider’s usage page, the tool server’s stdout, the framework’s debug output, and a terminal scrollback that closed with the window. Nobody can say what the agent saw on turn six.

A platform team runs four MCP servers for its coding agents: GitHub, internal docs search, a read-only Postgres server and the ticketing system. Someone wants per-tool rate limits and a log of which agent called what, so a gateway evaluation goes on the calendar. Nobody has asked the cheaper question yet, which is how much of that the reverse proxy they already run can handle.

Since July, quite a lot. The 2026-07-28 revision made MCP stateless at the protocol layer: the initialize handshake is gone, the Mcp-Session-Id header is gone, and every request carries its own protocol version, client info and capabilities. Two new headers, Mcp-Method and Mcp-Name, copy the JSON-RPC method and (for a tool call) the tool name out of the body and into headers, where proxies already look. Without sessions, an MCP server behaves much like any other HTTP API, which is why the difference between an API and MCP now comes down to who reads the docs. Before the revision, a proxy needed a body-parsing module to learn which tool was being called, and usually session affinity to keep each client on the backend holding its session. Now stock nginx covers the basics:

An agent calls a code-search tool and gets back a hundred hits. Each hit carries dozens of fields: node IDs, a URL for every related resource, avatar links, permission flags. The model needed three of them, a repo, a path and a snippet. The rest now sits in the context window for the remainder of the session, and the model reads past it on every later turn.

Tool definitions get most of the attention in agent token costs, and they’ve earned it. A server that exposes 150 tools puts 150 schemas in front of the model on every turn. Results are the other half of the bill, and they have fewer standard answers. An ordinary API client ignores the fields it doesn’t use, and ignoring is free. A model pays to read every token it’s handed.

Your agent test passes on your laptop and fails in CI. The laptop has a token for the issue tracker’s MCP server; CI doesn’t, and shouldn’t. Even with a token, the tracker listed three open bugs yesterday and lists four today, so the agent’s summary changes and the assertion on it breaks.

Web developers dealt with this years ago. VCR (Ruby), Polly.js, Betamax and go-vcr record real HTTP responses into a file the first time a test runs, then serve them from that file on every run after. The file is called a cassette. MCP needs the same thing: a proxy that sits between an agent and its MCP servers, writes every exchange into one cassette, and plays it back later with no server, no credentials and no network. mcprec below is an illustrative name for a tool you’d have to build.

An agent works through a ticket and asks a docs-search tool the same question on turn 3, again on turn 14, and again the next morning in a fresh session. Each repeat costs a charge against the upstream’s rate limit and a wait with the model idle, for an answer that never changed. Agents retry after errors and start every session by looking up what the last one already knew.

Form.io has released an MCP Server, Skills library, and Agentic Coding Plugin designed to bring schema-governed infrastructure to AI coding environments — extending its existing Universal Agent Gateway into the build-time layer. The toolset targets enterprise development teams using agentic coding tools such as Claude Code, Cursor, and Windsurf, where the default outcome is speed without standardization.

The problem Form.io is addressing is real and increasingly visible. As agentic coding accelerates across large organizations, applications generated by independent teams diverge in architecture, data handling, and compliance posture. Each agent makes its own decisions; five teams produce five incompatible implementations of the same solution. The efficiency gains nominally promised by agentic coding are offset by the governance overhead required to manage the fragmentation afterward.