Topic
Quick answer: When an MCP tool returns a large result set in one response, the practical limit is not abstract; transport frames, model context windows, and client memory all cap
Read articleQuick answer: A public MCP registry is a great home for a general-purpose tool you want strangers to discover, but it is the wrong place to expose an internal API
Read articleQuick answer: Agents love to fire several API calls in parallel because it feels fast, and for independent reads it is, but parallel writes against the same resource are a
Read articleQuick answer: A blocking MCP tool that wraps a long-running operation is a design error that shows up as a hung agent: the model waits on a single call that
Read articleQuick answer: A registry is a discovery channel, not a guarantee of availability. If your agent workflow depends on a server pulled from a public catalog and that entry is
Read articleQuick answer: Pasting a large OpenAPI document into an AI editor does not give the model better API knowledge; it burns the context window on schemas the current task never
Read articleQuick answer: An MCP tool with a loose input schema — everything typed as a string, required fields left unmarked, or a catch-all object — pushes validation to runtime and invites the
Read articleQuick answer: Pointing an agent at a registry entry that always resolves to "latest" means the tools it calls can change underneath you without a commit or review,
Read articleQuick answer: Cursor rules and an OpenAPI contract are two sources of truth that will drift the moment they are edited separately, and the agent ends up following whichever is
Read articleQuick answer: An MCP server can expose every operation correctly and still fail in practice, because the model chooses a tool by reading its name and description. When descriptions are
Read articleQuick answer: A Model Context Protocol (MCP) server exposes an API's operations as tools that an AI agent can discover and call, with a typed input schema and
Read articleA tool call returned truncated JSON on timeout. The agent treated the partial chunk as live data and completed the missing fields by guess, so invented values blended into the plan. Covers why a model should never finish a partial record, and the raw-output checkpoint that stops it.
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