Can Your Bot Trust an MCP Progress Notification?
MCP progress updates are optional during a tool call. Learn what they prove, how current Tasks differ, and when a bot should verify the final result.
Practical guides to AI agents, workflows, tools and permissions. Original, sourced explanations from BotBento.
MCP progress updates are optional during a tool call. Learn what they prove, how current Tasks differ, and when a bot should verify the final result.
MCP cancellation asks a server to stop work, but it may still finish. Learn the current stdio and HTTP rules and how to check side effects.
A practical Stripe and GitHub example for limiting a bot's credential to its job, with the permissions to verify and the failure paths to test.
MCP Roots is deprecated in the 2026-07-28 spec. Learn what remains supported, why roots are advisory, and how to migrate file scoping safely.
A scheduled bot can miss its start, fail to reach its target, or stall after starting. Each silence needs a different signal and a clear recovery path.
A practical guide to MCP tool-call timeouts: distinguish protocol guidance from client defaults, measure real durations, and use asynchronous jobs for slow work.
MCP tools can declare an outputSchema and return structuredContent. Here is how to validate successful results without confusing tool errors with transport failures.
The MCP 2026-07-28 spec removed session IDs and the initialize handshake. Here's the explicit-handle pattern that replaces them, and what breaks if you don't update.
A bot that books a flight then fails to book a hotel has left the world in a half-finished state. Here is how to design the undo step.
OAuth token revocation can have a propagation delay. Learn what the standard guarantees, how validation affects the result, and how to verify a bot has stopped using a tool.
Read MCP protocol errors, isError, structuredContent and real-world readback separately before telling a user a tool action succeeded.
A bot saying 'done' is not proof. Here is how to check completion independently, with a worked example and what evidence to keep.
A concrete rule for choosing between one agent with many tools and a supervisor that delegates to specialist agents, with a worked example.
MCP's sampling feature, which let servers ask your client to run an LLM call, is now deprecated. Here is what that means for bots built on it.
Learn how an AI bot should classify rate limits, honor Retry-After, retry safely with jitter and a cap, and report an unfinished tool action.
MCP elicitation lets a connected tool pause and ask a question mid-task. Here's what the spec allows it to request, and what it must route elsewhere.
A timed-out tool call can secretly succeed. See why MCP's idempotentHint doesn't protect you, and what an idempotency key actually does.
A practical checklist for checking source ownership, dates, contradictions and unresolved questions in AI-drafted research notes before you rely on them.
MCP's HTTP authorization spec requires OAuth resource indicators. Learn what they protect, where implementation can fail, and what to check before connecting a bot.
Installing a tool on a bot and letting it act on your data are two separate steps. Here is where the line sits, with a worked example.
Grok Bot's group chats hold bots only. BotBento is built so people and bots share one room under one permission model. Here is the reasoning and the honest status.
Test a calendar AI agent with a disposable event, explicit time zone, denied write and revoked connection. Check the saved result before trusting its reply.
Define when an AI agent should finish, pause or stop at a limit, with an illustrative research task and practical checks for loops and unresolved work.
Choose where an AI agent runs by separating its model, tools and scheduler. Compare local, cloud and hybrid setups with a practical decision checklist.
Design a useful AI agent run record with evidence, partial results, retry decisions and clear next steps, using an illustrative research routine.
Understand MCP servers, authorization and tool approval with an illustrative calendar example and a practical connection review checklist.
A practical way to choose between an AI agent and a fixed workflow, with a weekly research example, stopping rules and an evaluation checklist.