Topic
You already have a working REST API. Hand-writing and maintaining a second, parallel set of AI tool wrappers is pure transcription that drifts. Generate MCP tools from your OpenAPI contract, start read-only, and keep authorization where it belongs - in your API.
Read articleYour AI coding assistant keeps using pageSize instead of limit and data.items instead of data.records because it guesses from pasted docs. Give it a local OpenAPI spec it can query over MCP, then verify multi-step scenarios against real requests and mock the missing dependency.
Read articleMCP connects one agent to tools and data. A2A connects autonomous agents to each other through discoverable, long-lived tasks. They are not competitors; they sit at different layers, and getting the boundary wrong is where agent architectures become unmaintainable.
Read articlePointing an agent at forty internal MCP servers breaks tool limits and buries it in duplicated names. The pattern that works at scale is one authenticated gateway that aggregates specs, namespaces the tools, and keeps auth, logging, and rate limits in one place.
Read articleMCP turns external content into actions: a tool description or API response is text the model reads and trusts, which makes indirect prompt injection a real attack path. Here is a practical threat model and the controls that actually work, from scoped tokens to human-in-the-loop and audit logs.
Read articleMost teams expose everything as MCP tools and end up with a catalog the model cannot navigate. The protocol defines three primitives for a reason: tools act, resources are readable context, and prompts package reusable workflows. Here is how to decide, with examples from an API platform.
Read articleAgents discover MCP tools at runtime and learn names and parameters from descriptions, so a renamed tool is a silent production break. Here is how to evolve a tool catalog: additive changes, deprecation windows, versioned names as a last resort, and CI diffs from the source spec.
Read articleEvery AI editor now speaks MCP, but each one stores config differently, supports different transports, and has its own OAuth quirks. This is the practical comparison: where the config lives, what works over stdio vs HTTP, and the gotchas for six mainstream clients.
Read articleHuman callers read prose and ask a teammate when confused; agents retry, chain calls, and act on whatever a response most resembles. A few design rules separate APIs agents use reliably from ones they misuse: idempotency, strict schemas, machine errors, and async jobs.
Read articleAn MCP server is just JSON-RPC over a transport, which means it is fully testable without an AI in the loop. Here is a practical ladder: Inspector for discovery, curl for the protocol, and deterministic automated tests for schemas, errors, and side effects in CI.
Read articleThe same MCP server can run over stdio on a laptop or Streamable HTTP behind a domain, and the choice changes auth, state, latency, and who can use it. Here is a decision framework with the actual launch commands and client config for each transport.
Read articleLocal stdio MCP servers inherit your CLI credentials, so nobody notices auth until they host one. Remote MCP servers require OAuth 2.1 with PKCE, metadata discovery, and short-lived tokens. Here is the full flow and the mistakes that get servers rejected by clients.
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