Model Context Protocol — an open standard for exposing APIs as typed tools that LLM agents can call. It was introduced by Anthropic and is now supported by a range of agent frameworks and IDEs.

Is llms.txt a formal standard?

No — it's a community convention (llmstxt.org) that most major AI crawlers now respect. It sits alongside robots.txt and sitemap.xml, not replacing them.

Do I need MCP if I already have an OpenAPI spec?

OpenAPI describes the API; MCP lets an agent actually invoke it inside its runtime with typed tool calls, permission scopes, and streaming. They're complementary.

How do idempotency keys help agents?

An agent that retries after a timeout can safely resend the same request with the same key — the server returns the original result instead of creating a duplicate.

Can I try Venly Finance's MCP server?

Yes — the endpoint and connection instructions are on /developers, alongside the llms.txt manifest and the OpenAPI spec.

Agent-Ready APIs & MCP: Designing APIs for LLM Consumption

Agent-ready APIs are designed so LLM agents can call them safely without a human intermediary. The three pillars are an MCP server (typed tools), llms.txt (discovery), and idempotent primitives (safe retries).

An "agent-ready" API is one that an LLM-based agent can discover, understand, and operate without a human translator. In practice that means three things: (1) a Model Context Protocol (MCP) server that exposes typed tools to the agent, (2) an llms.txt manifest so crawlers and assistants can find your canonical docs, and (3) API design primitives — idempotency keys, typed errors, deterministic pagination — that make agent retries safe.

How it works

1. Publish `/llms.txt`. A short index at the site root pointing agents to your best canonical docs, endpoints, and glossaries. Publish `/llms-full.txt` with the full expanded content. 2. Ship an MCP server. Expose your API as a set of typed tools (`create_transfer`, `get_balance`, ...) with JSON schemas the agent can introspect. Model Context Protocol is the emerging standard, backed by Anthropic and adopted by IDE/agent tooling. 3. Design for idempotency. Every state-changing endpoint accepts an `Idempotency-Key` header so an agent that retries after a network blip does not double-post a transfer. 4. Use typed errors. Return `error.code` strings (e.g. `insufficient_funds`, `rate_limit_exceeded`) — agents can branch on them; free-text messages force LLMs to guess. 5. Keep OpenAPI honest. A generated OpenAPI 3.1 spec that always matches production lets any agent framework auto-build clients.

Who it''s for

Platform teams whose customers want to embed the product inside agent workflows
Developer-experience teams evaluating MCP for their own APIs
Solution architects at PSPs, marketplaces, and fintechs designing internal AI copilots

Why it matters for payments

Payments are the highest-stakes surface for agent-driven APIs: a hallucinated retry, a misread balance, or an ambiguous error can lose real money. Idempotency keys + typed errors + a narrow MCP tool surface bound the blast radius and make agent-driven flows auditable.

What Venly Finance ships

`/llms.txt` and `/llms-full.txt` regenerated from our live content
An MCP server exposing the Finance API as typed tools
OpenAPI 3.1 spec generated from the same source as the runtime
Idempotency keys on every write endpoint; documented error codes

Sources

Anthropic, "Model Context Protocol (MCP)" specification
llmstxt.org — proposed `/llms.txt` convention
OpenAPI 3.1 specification
Stripe engineering blog, "Designing robust and predictable APIs with idempotency"