Errors the model can recover from, and checks in code.
Errors the model can recover from
An error message is the only teacher the model has in the middle of a run. The example sends 20 slightly wrong calls (a date written 3/4/2026, a status written Open, an id missing its prefix, an amount written $20, a wrong argument name), returns an error, and lets the "model" try again up to twice:
Error message
Fixed on retry
Error: invalid arguments.0%
Error: invalid value for 'since'.25%
Error: invalid value for 'since': expected YYYY-MM-DD (example: 2026-03-04), got '3/4/2026'.100%
The third message names the argument, what was expected, an example, and what was received . For "not found" errors, add the next step ("no order ORD-9999; use search_orders to find the id"). Two cautions: the repair rules in the example were written for exactly these slips, so 100% is what a clear message makes possible , not what to expect; and a call that passes validation is not necessarily the right call (replacing "defective" with "other" is valid but loses information).
Also decide what each tool returns on success: short, structured enough to use next, and not a 10,000-word dump that fills the history (module 1's prompt growth).
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What the error message and the checks are worth Wrong calls fixed on retry (20 slips, up to 2 repairs) "invalid arguments" 0% names the field 25% field + expected format + example 100% Wrong refunds that moved money 7 calls, 1,130 paid out no checks 0 calls, 0 schema + rules Emails after a lost reply and 2 retries 3 sent no key 1 sent idempotency key Repair rules were written for these 20 slips, so 100% is what clear messages allow at best, not what to expect. What the error message and the checks are worth Checks in code, not in the prompt
A tool description that says "confirm with the user first" is a request, not a guarantee. Anything with a real effect needs checks that run in code :
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Between the model's call and the real effect: four checks, each with a message back Model call name + JSON arguments Schema check types, enums, required, formats Business rules exists? allowed? within limits? Preview risky tools say what they would do Run once idempotency key, then the real effect A refusal goes back as the observation what was wrong, what was expected, an example, and what to try next Result text short, useful The name and description are what the model reads to choose the tool in the first place. Say what it does, when to use it, when not to, and what each argument looks like. Never rely on the model alone to keep a risky tool safe: the checks are code. Four checks between the model's call and the real effect
Schema validation : required arguments, types, enums, formats. Cheap, catches most slips, produces the messages above.Business rules : things a schema cannot say: the order exists, it is not cancelled, the amount does not exceed what is left.Preview or confirmation for anything risky or irreversible: the first call returns "would refund 20.00 on ORD-1042"; only a second call with confirm=true acts, ideally after a human agrees.Idempotency : if the same call arrives twice (a retry after a lost reply), it must not happen twice.
What these buy in the example:
Situation
Without
With
10 refund calls, 8 of them wrong (negative amount, bigger than the order, cancelled order, a string instead of a number, an unknown reason)
7 wrong calls changed money, 1,130.00 paid out
0 wrong calls, 0.00
an email goes out, the reply is lost, the agent retries twice
3 emails sent
1 email (idempotency key)
A key is any stable id for the intended action (run id plus step number, for example) that the tool remembers. A tool that costs money, sends messages or deletes data should refuse to run twice for the same key.
Name : verb first, specific, unique. No two tools that could be confused.Description : what it does, when to use it, when not to, side effects, in user words.Parameters : enums for closed sets; an example for every format; optional only when truly optional.Result : short; says what was done.Errors : say what was wrong, what was expected, what was received, what to do next.Checks : schema, rules, preview for risky actions, idempotency for effects.Fewest tools : every tool you remove is one fewer wrong choice.Held-out test : real phrasings you did not write the descriptions for.
Interview angle
"Your agent keeps calling the wrong tool, or calls the right one with bad arguments. What do you check?" Start from the tool text the model sees : are names specific, are descriptions saying when to use and when not to, are two tools nearly identical, are there too many tools in play (retrieve a few per query)? For bad arguments: do the schema and the error messages tell the model what was expected, with an example? Then measure : log the model's tool choices on real requests, build a held-out list, change one description at a time and re-run, and keep descriptions from overfitting to the examples you looked at. For anything with side effects, add code-level validation, a preview or confirmation step, and idempotency keys, because prompts do not enforce rules.
Check yourself
Two tools, get_product and check_stock, both mention a product code. Which words in their descriptions should tell the model apart? (What each one is for and what it is not for: "full details, not stock levels" against "how many are left right now".)
For the curious What the checks do not coverThe schema check refuses NaN, booleans, strings and zero for an amount, and the business rules refuse more than is left to refund. A check in code can still not tell that a legitimate-looking refund is the wrong one, which is why the money-moving tool also returns a preview and waits for confirmation. The unchecked version is deliberately unsafe: it is there to show what the checks save.