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Most AI tool-calling failures are contract failures, not mysterious model behavior. A reliable fix is to trace the complete exchange—tool declaration, model-generated call, application execution, tool result, and follow-up context—then correct the first boundary that violates the contract. Start by logging the raw request and response, classify the error, validate the schema, verify call and result identifiers, and only then change prompting or retry logic.
What an AI tool call actually is
A tool call is a protocol exchange between your application and a model. The model does not execute your function directly. It selects a declared tool and emits structured arguments; your application validates and runs those arguments; your application sends a result tied to the original call; the model then receives that result in the conversation context.
That creates five separate failure boundaries:
- Declaration: the tool name, description, parameter schema, and provider-specific options must be valid.
- Model output: the selected name and arguments must match the declaration.
- Application execution: your code must authorize, validate, and run the requested operation safely.
- Tool result: the result must use the required envelope and the original call identifier.
- Follow-up context: provider-specific assistant blocks, signatures, and parallel results must be preserved.
A failure at one boundary can look like a failure at another. For example, a model that appears to “ignore” a tool may actually be receiving a malformed result, while a 400 returned after a successful function execution may be caused by altered hidden-thinking blocks in the next request.
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Classify the error before changing code
Save the complete request, response, model version, tool declarations, conversation messages, HTTP status, and provider request ID (when available). Redact secrets and personal data, but do not discard the raw tool-call envelope or the exact schema used for the failing request.
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| Symptom or status | Likely class | First check | Correct response |
|---|---|---|---|
| HTTP 400 | Validation, syntax, schema, or context error | Provider error body and the exact serialized request | Fix the declaration or message sequence; do not retry unchanged |
| HTTP 401 | Authentication | API key, token audience, environment, and expiry | Refresh or replace credentials, then test a minimal request |
| HTTP 403 | Permission or policy | Project, model, tool, and resource permissions | Grant the required permission or use an authorized resource |
| HTTP 402 | Billing or account state | Account balance, payment status, and quota | Resolve billing or quota; retrying does not repair this class |
| Malformed or undeclared call | Model output does not match the declaration | Tool name, argument JSON, and schema subset | Make names and descriptions unambiguous; enforce strict validation where supported |
| Function runs but model ignores result | Result-envelope or context error | Call ID, result role/type, sibling results, and preserved context blocks | Return the result in the provider’s required format and continue from the complete response |
| HTTP 408, 429, or 5xx | Transient transport or service failure | Retry headers, attempt count, and idempotency | Use bounded exponential backoff with jitter and an operation-safe retry policy |
Do not classify every failure as a “bad prompt.” A 400, 402, or 403 normally needs a contract, billing, or permission fix before any retry. A 429 or 5xx can be transient, but a retry is safe only when repeating the operation cannot create an unwanted side effect—or when the operation has an idempotency key.
Use a repeatable diagnostic sequence
- Capture the boundary that failed. Record the outgoing declaration and messages, the raw model response, the exact function arguments passed to your code, the function result, and the next request. Log a correlation ID for the whole turn and the provider’s call ID for each tool call.
- Reduce to one tool. Temporarily remove unrelated tools, multimodal content, and optional parameters. A minimal request tells you whether the issue is schema-level or interaction-level.
- Validate the declaration independently. Check JSON syntax, supported schema keywords, property names, required fields, nesting depth, and whether every tool name is unique.
- Inspect the model’s raw call. Confirm that the emitted name is declared exactly and that arguments are valid JSON of the expected type. Do not rely only on a deserialized object: the raw response can reveal truncated or duplicated output.
- Validate at the application boundary. Re-check types, ranges, authorization, and resource ownership before executing. Return a structured validation error to the model instead of silently dropping an invalid request.
- Match the result to the call. Copy the original call identifier exactly. In a parallel response, account for every sibling call rather than returning only the first result.
- Rebuild the continuation from the complete provider response. Preserve required assistant content, thinking or redacted-thinking blocks, and signature metadata. Do not reconstruct the response from only the visible text.
- Retry only the right class. Apply bounded backoff to transient failures; stop immediately on ordinary validation, authentication, permission, or billing errors.
Fix tool declarations and schemas
OpenAI strict function schemas
OpenAI’s strict mode is intended to keep generated arguments aligned with the declared JSON Schema. In strict mode, set additionalProperties to false and mark every property as required. An argument that is logically optional should be represented with a nullable type (for example, a string or null) while remaining listed in required. OpenAI’s function-calling documentation recommends always enabling strict mode.
{
"type": "function",
"name": "lookup_order",
"description": "Find one order by its exact order ID for the authenticated customer.",
"strict": true,
"parameters": {
"type": "object",
"additionalProperties": false,
"properties": {
"order_id": { "type": "string" },
"include_items": { "type": ["boolean", "null"] }
},
"required": ["order_id", "include_items"]
}
}A common mistake is declaring include_items as optional by omitting it from required. That may be valid in ordinary JSON Schema, but it conflicts with OpenAI strict-mode requirements. Handle the explicit null value in application code.
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Google Gemini schemas
Gemini function declarations implement a supported subset of OpenAPI rather than every JSON-Schema feature. Very large or deeply nested declarations can be rejected even when they are valid under a general-purpose schema validator. Simplify nested objects, remove unsupported keywords, split an oversized tool into narrower operations, and keep descriptions focused on the decision the model must make.
When a Gemini error names malformed_function_call, malformed_tool_call, unexpected_tool_call, or too_many_tool_calls, treat the code as a classification signal. Inspect the corresponding call and declaration instead of applying a generic retry.
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Anthropic Claude schemas
Claude is more likely to choose the wrong tool when names are duplicated or descriptions are generic. Give every tool a unique, specific name, state when it should be used and when it should not be used, and include a concrete input example for complicated arguments. Use strict: true when your schema fits the supported subset and you need stricter argument conformance.
Do not expose several tools that differ only by an obscure parameter. Either combine them behind one clearly described operation or make the distinction explicit in each description. A name such as search is less useful than search_customer_orders when other search operations are present.
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Tool selection improves when the model can distinguish tools by purpose, authorization scope, and expected inputs. For each declaration:
- Use a unique verb-and-object name, such as
create_calendar_eventrather thanrun. - Describe the triggering condition and the non-triggering condition.
- State which identifiers are accepted and their format.
- Include one short valid input example for irregular or nested arguments.
- Keep destructive operations separate from read-only operations and say that confirmation is required before side effects.
- Remove duplicate or obsolete tools from the request; a model cannot reliably distinguish tools that your application no longer supports.
If the application requires zero or one function call in a turn, configure OpenAI with parallel_tool_calls: false. This prevents an unexpected batch of calls that your executor is not prepared to coordinate. OpenAI built-in tools cannot be placed in a parallel function-call batch, so keep those paths separate.
Return tool results in the exact envelope
Your application, not the model, executes the function. The result must be attached to the original call identifier and sent in the role or content type required by the provider. Never replace a structured result with an unlabelled assistant message.
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Parallel calls
When a model emits several calls, execute each permitted call, collect each result, and return all result blocks in the single user message required by the provider. Anthropic specifically requires every parallel tool_result block in one user message. Sending separate user messages can teach Claude that parallel use is unsupported and can also break the continuation.
Gemini requires the function result to carry the same function-call ID. If the response contains multiple required parts, preserve them all; returning only one result can produce a mismatch even when that function executed successfully.
Structured success and failure
Use a compact, stable result shape so the model can decide what to do next. Include machine-readable fields and a human-readable explanation only where it helps.
{
"ok": false,
"error": {
"code": "ORDER_NOT_FOUND",
"message": "No order with this ID belongs to the authenticated customer.",
"retryable": false
}
}Validate arguments and permissions before a side effect. If validation fails, return ok: false with a stable error code; do not throw an unstructured stack trace into the conversation. Mark errors as retryable only when a retry can plausibly change the outcome. For writes, use an idempotency key or a server-side deduplication record so a transport retry cannot create duplicate charges, tickets, or messages.
Preserve provider-specific conversation context
Anthropic: thinking and redacted-thinking blocks
When continuing a Claude tool conversation, send the latest assistant thinking and redacted-thinking blocks unchanged. A 400 after a tool call often means those blocks were altered, omitted, reordered, or rebuilt from a text-only representation. Store the complete assistant response object and append the tool results to that exact context rather than serializing only the visible answer.
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Gemini: thought signatures
Gemini combined-tool flows may require thought signatures for all required response parts. Reuse the full response object through the SDK when possible; it can preserve signatures that are easy to lose when you manually copy text and function calls. A missing_thought_signature error indicates a continuation problem, not a function implementation problem.
OpenAI: call and batch state
Keep the identifier returned for each function call until its result has been accepted. If parallel calls are enabled, maintain a map from call ID to execution result and do not drop calls that finish later. If your application cannot maintain that state safely, disable parallel calls for the affected request path.
Retry transient failures without duplicating side effects
Use bounded exponential backoff with jitter for HTTP 429, 408, and 5xx responses. A typical policy increases the delay after each attempt, adds a random component so many workers do not retry simultaneously, and stops after a small fixed number of attempts or a deadline. Honor a provider-supplied retry-after value when present.
Google’s current documentation says its Python SDK retries transient failures up to four times, with documented delays ranging from approximately one second initially to 60 seconds maximum. That is SDK behavior, not a universal guarantee for every language or endpoint; if you add an application retry layer, account for retries already performed by the SDK to avoid multiplying attempts.
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- Do not blindly retry 400, 402, or 403 responses.
- Use idempotency keys for writes and make read operations safe to repeat.
- Record attempt number, delay, status, and final outcome for incident analysis.
- After the retry budget is exhausted, return a structured temporary-failure result or an operator-visible error; do not silently ask the model to repeat indefinitely.
Provider comparison at a glance
| Area | OpenAI | Anthropic Claude | Google Gemini |
|---|---|---|---|
| Schema and strictness | Strict mode requires additionalProperties: false and every property required; nullable types represent optional values. |
Use strict: true when the schema fits the supported subset; sharper descriptions and examples reduce wrong-tool calls. |
Uses a supported OpenAPI subset; large or deeply nested declarations may be rejected. |
| Call/result identifier | Preserve each function-call ID, including IDs in parallel batches. | Return each tool_result for its matching call and keep parallel results together. |
Send the function result with the same function-call ID. |
| Parallel envelope | Set parallel_tool_calls: false when one-or-zero calls are required; built-in tools cannot join a parallel function batch. |
Put all parallel result blocks in one user message. | Preserve every required response part and associated signature. |
| Hidden context | Retain the complete response and call state needed by the continuation. | Do not alter assistant thinking or redacted-thinking blocks. | Preserve thought signatures; reusing the full SDK response is safest. |
| Useful error signals | HTTP validation errors and MCP item error fields distinguish protocol, execution, and connectivity problems. |
400 responses after continuation commonly indicate incorrectly formatted tool results or changed context blocks. | Codes include malformed_function_call, malformed_tool_call, unexpected_tool_call, too_many_tool_calls, and missing_thought_signature. |
| Documented retry behavior | Apply an application policy for transient 429, 408, and 5xx responses. | Apply an application policy for transient transport failures. | The Python SDK documents up to four transient retries with approximately one-second to 60-second delays. |
Common failure patterns and their fixes
“The model invented a parameter.”
Check whether the parameter is absent from the schema, optional when strict mode requires it, or described ambiguously. Turn on strict validation where supported, set OpenAI’s additional-properties rule correctly, and return a structured validation error if an unknown field still arrives.
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“The function succeeded, but the model says it has no result.”
Verify that the result used the original call ID, the required role or content type, and the complete continuation context. For parallel calls, confirm that every sibling result was included in one provider-compliant envelope.
“The provider returns 400 immediately after I send a tool result.”
Compare the successful model response with the response object you stored. On Claude, restore unchanged thinking and redacted-thinking blocks. On Gemini, restore all required thought signatures and response parts. On every provider, check that the result references a currently pending call rather than a regenerated or truncated ID.
“A retry created the same record twice.”
The operation was not idempotent. Add an idempotency key derived from the logical user action, persist the first completed result, and return that result when the same key is received again. Limit retries to the transport layer and do not ask the model to repeat a side effect blindly.
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Do not assume JSON-Schema portability. Generate provider-specific declarations from one internal contract, then remove unsupported keywords and simplify nesting for Gemini. Keep provider adapters and contract tests so a schema change is checked against each target API before deployment.
Build observability into the tool layer
Useful logs answer what the model requested, what the application executed, and what the provider accepted. For every turn, record:
- provider, model, timestamp, and correlation ID;
- tool names and a redacted copy of each declaration;
- raw call name, arguments, call ID, and response parts;
- authorization decision and validation outcome;
- execution duration, result size, structured error code, and retryability;
- HTTP status, provider request ID, retry count, and final disposition.
Keep sensitive argument values out of ordinary logs, but retain enough shape information to reproduce schema and envelope bugs. Add automated tests for unknown properties, missing required values, wrong call IDs, parallel batches, malformed results, permission denials, transient retries, and duplicate side-effect attempts. These tests catch protocol regressions before a prompt change makes them visible in production.
A production checklist
- Every tool has a unique name and a precise trigger description.
- The declaration uses only the target provider’s supported schema subset.
- OpenAI strict schemas set
additionalProperties: falseand require every property, using nullable types for optional values. - Arguments are validated and authorized before execution.
- Every result carries the exact originating call ID.
- All parallel results are returned in the provider-required envelope.
- Claude thinking and redacted-thinking blocks remain unchanged.
- Gemini thought signatures and required response parts are preserved.
- Parallel calls are disabled where the executor cannot safely coordinate them.
- Transient retries use bounded backoff, jitter, and idempotency protection.
- 400, 401, 402, and 403 failures stop for diagnosis instead of being retried unchanged.
- Logs contain enough raw protocol data to identify the first broken boundary without exposing secrets.
When to change the model or prompt
Change prompting only after the declaration, envelope, and context are valid. If the model still chooses incorrectly, narrow the tool set, make names more specific, add a concrete input example, and state authorization or confirmation requirements. If a tool remains unreliable because its inputs are inherently ambiguous, redesign the interface: replace free-form strings with enumerated values, split a multi-purpose function into explicit operations, or move deterministic routing into application code. A smaller, clearer contract is usually more dependable than a longer instruction appended to an invalid one.
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