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A max_retries=2 setting does not tell you how many times your tool function runs. The number belongs to a specific component, it may count retries rather than total attempts, and it may repeat a network request without re-running your tool at all. Whether a tool runs exactly once depends on which layer retries, what gets repeated, and whether the gateway documents an execution guarantee. A bare claim that a gateway calls your tool exactly once, with no product name or documentation attached, cannot be verified from the setting alone.
Why the claim cannot be checked as written
The statement names a setting and a guarantee, but not the gateway that makes either one. Without that name, there is no documentation to read, no changelog to check, and no default value to confirm. Treat the sentence as a hypothesis to test rather than a fact to rely on.
The phrase “tool gateway” also covers several different things. Some gateways sit between your application and a model provider. Others sit between a model and your tools. Some do both. Each role can have its own retry logic, and a single number in a configuration file may apply to only one of them.
Retry settings live in different layers
The same word, “retry,” describes three behaviors that have very different effects on your code. The table below separates them.
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| Layer | What gets repeated | Does your tool function run again? | Who owns the setting |
|---|---|---|---|
| Client or SDK HTTP retry | A single HTTP request made by a client library | No. The function that made the call is not re-entered, but the same request can reach the server twice. | The client library. The OpenAI Python SDK documents its own retry control for this purpose. |
| Gateway retry | A provider request, or a switch to a fallback provider | Not stated for the unnamed gateway. It depends on how the gateway implements its own loop. | The gateway product |
| Framework tool retry | A tool call that failed validation or raised a retry signal, plus a message sent back to the model | Possibly. The model may issue a new tool call with corrected arguments. | The agent framework. Pydantic AI documents per-tool, per-toolset, per-run, and agent-wide limits. |
Client or SDK retries
A client-level retry repeats a failed or timed-out HTTP request. Your tool function is usually not involved, so the function body is not re-run by this mechanism. The risk is at the request boundary. If a request times out after the server has already acted on it, the retry sends the same request again, and the server may act a second time. This is the most common route by which an operation with side effects runs more than once, even when no one wrote a retry loop around the tool.
Gateway retries
A gateway may retry a provider request, move to a fallback model or provider, or do both. Whether any of this re-runs a tool depends on where the tool executes. If the gateway only returns model output and your application executes the tool, a gateway retry of the model request does not by itself execute your tool again. If the gateway also executes tools, the picture changes.
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AcruxCore’s documentation separates constructor-level HTTP retries from gateway-level behavior. It is a useful example of how a vendor draws that line, but it says nothing about the gateway in the title, which is not named.
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This is the layer most likely to re-run a tool. Pydantic AI’s documentation, in the section titled “Requesting a Tool Retry,” states: “Raising ModelRetry generates a RetryPromptPart containing the exception message. That prompt is sent back to the LLM so it can correct the parameters and retry the tool call.”
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In practice, this means a validation failure or an explicit retry signal inside a tool can send a message back to the model, and the model may call the tool again with different arguments. The tool body runs again because the model asked for another call, not because a network layer repeated a request. Pydantic AI’s documentation describes limits that can be set per tool, per toolset, per run, and agent-wide, so the number that matters may not be the one named in a gateway’s settings.
What the number 2 means
Even within one layer, a value of 2 can mean different things. Read the component’s documentation to find out which of these applies:
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- Extra retries: up to 2 retries after the first attempt, for up to 3 attempts in total.
- Total attempts: up to 2 attempts in total, meaning one retry at most.
- Per-operation or per-request: the limit applies to one request, one tool call, or one run, and the total across a session or agent may be higher.
- Not a retry count at all: some settings control backoff, timeouts, or fallback choices, and the field name is only a convenient label.
The field name alone does not settle the question. Two systems that both use max_retries=2 can produce different execution counts for the same tool.
How to check a gateway’s exactly-once claim
If you are evaluating a gateway that makes this promise, work through these steps before relying on it:
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- Identify the product, its version, and the component that owns
max_retries. Note whether the setting is in a client constructor, a gateway configuration file, or an agent definition. - Find out what the setting repeats: an HTTP request, a provider call, a fallback, or a tool invocation.
- Determine whether the number means extra retries or total attempts, and whether it applies per request, per tool, per run, or globally.
- Look for a documented execution guarantee or deduplication behavior. A claim of exactly-once execution should cite a mechanism, such as an idempotency key that the gateway checks before running the tool. A retry limit by itself is not evidence of such a mechanism.
- Test the claim in a staging environment. Give your tool a unique invocation ID, log each run, and force a timeout or validation failure. Count the log entries. If the count exceeds one when the gateway reports one call, the claim does not hold for that path.
Designing tools that survive retries
Whatever the gateway promises, a tool with side effects is safer when a second execution does no harm. Accept an idempotency key from the caller, record completed keys, and return the stored result when a key repeats. Keep validation errors separate from side-effecting work, so a model correcting its arguments does not trigger a charge, an email, or a database write before the arguments are final. These are general engineering practices, not guarantees made by any particular gateway.
When you cannot confirm a single execution, treat the tool as at-least-once and design for it. That is a more reliable position than trusting a configuration value.
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