To stop an AI agent from calling a tool that does not exist, resolve every model-emitted tool name by exact lookup in the active, application-controlled registry. Reject unknown names without dispatching them; for a match, validate arguments against that tool’s contract, then check authorization and any required approval before execution.
This is deterministic resolution, not tool selection. Selection is the model’s choice of which available capability might help. Resolution is the application’s check that the requested name binds to a real tool in the registry for this request, and that the call satisfies that tool’s declared interface. Selection can be poor even when it picks a real tool; resolution prevents an unregistered or incompatible call from reaching a handler.
What deterministic name resolution does—and does not do
A tool call is a structured request from the model for the application to perform an action. The application, not the model, runs the implementation and returns a result associated with that call. In OpenAI’s documented flow, for example, tool output refers to the initiating call with a call_id. The call should therefore be treated as untrusted input crossing an application boundary, not as permission to execute a function merely because it looks plausible. OpenAI’s function-calling guide describes this flow.
A reliable boundary separates three questions:
- Existence: Does the emitted name resolve to exactly one tool in the active registry?
- Contract: Do the arguments parse and satisfy that tool’s schema or signature?
- Permission: May this caller perform this operation on this target resource now?
The first two checks establish that a call maps to a known interface. They do not establish that the request is semantically sensible or authorized. A valid call can still be harmful, inappropriate, or aimed at a resource the user cannot access.
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How to implement a deterministic resolver
1. Bind calls to a canonical, active registry
Maintain an application-controlled registry keyed by canonical tool name. Each entry should bind the public name presented to the model to one implementation, one input contract, and an explicit version. Record which registry snapshot was supplied for the current request or turn; otherwise, a correct lookup against a stale or unrelated catalog can still dispatch the wrong implementation.
This registry-and-signature arrangement is a practical architecture, not a format mandated by every tool-calling protocol. The essential property is that the resolver uses the same active definitions the application exposed for the call, rather than inferring a function from the name or searching a broader catalog at execution time.
2. Resolve the name exactly; fail closed
For every returned call, look up its name in that active registry. If there is no match, do not dispatch anything. Return a bounded error the model can use to recover, or ask it to choose from the tools actually available. Do not autocorrect a misspelling to the “closest” handler: a fuzzy match can silently turn an invalid request into a real side effect.
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If backward compatibility requires aliases, declare each alias explicitly and map it to exactly one canonical entry. Reject ambiguous aliases. The reviewed platform documentation does not establish a cross-platform alias convention, so alias behavior should be an application policy rather than an assumed provider feature. The closed-world approach of checking registry membership is also the central proposal in the 2026 preprint “Closed-World Resolution Against Tool Hallucination in LLM Agents.”
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Only after resolving the name should the application parse the argument payload and validate it against that entry’s contract. Reject malformed JSON, missing required properties, incorrect types, and fields the contract does not allow. Pass the validated representation—not an unchecked raw payload—to the handler.
For example, suppose the active registry defines get_weather with a required string field location. A call named get_weathr fails existence checking, so no handler runs. A call named get_weather with no location, or with an undeclared field, fails contract validation. A well-formed call can still fail the permission check if the user is not allowed to query that location. These are distinct gates, not interchangeable validation errors.
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4. Authorize the user, target, and operation before effects
After resolution and schema validation, enforce identity, tenant, target-resource, and operation-level permissions in the handler or a trusted guardrail. Apply least-privilege credentials, require human approval where the product’s policy calls for it, and avoid returning secrets or unnecessary data to the model.
Microsoft’s Foundry guidance puts the security boundary plainly: “Treat tool arguments and tool outputs as untrusted input.” Validate and sanitize values and account for unintended side effects. The OpenAI Agents SDK also cautions that request-scoped tool visibility does not substitute for authorization based on arguments or the target resource; enforce those checks inside execution or guardrails. See Microsoft Foundry’s function-calling guidance and the OpenAI Agents SDK tools guide.
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Track the call identifier, resolved canonical name, registry or schema version, validation result, authorization result, approval outcome when relevant, and handler result. Return output associated with the initiating call identifier wherever the platform requires it. Both OpenAI’s tool-flow documentation and Microsoft’s example describe connecting the tool result to the preceding call; Microsoft specifically instructs developers to replace the response placeholder with that earlier response’s call_id.
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6. Report failures by class
Separate errors in logs and metrics so operators can distinguish a model or integration issue from an access decision or handler outage. Give the model only the bounded information needed to recover; do not reveal secrets or internal registry details.
- Unknown tool name
- Malformed argument encoding
- Schema or signature mismatch
- Authorization denied
- Approval required or denied
- Timeout or handler failure
- Successful execution
Microsoft’s troubleshooting guidance associates missing tools with an absent agent definition or poor naming, invalid JSON with schema mismatch or incorrect model output, and wrong parameters with ambiguous tool descriptions. Those are useful diagnostic leads, not replacements for the application-side checks above.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where provider strict schemas fit
Provider-side structured-output enforcement can reduce malformed calls, but it does not replace the application’s binding of a returned name to its registered implementation or its permission checks. Schema behavior varies by provider, API surface, tool type, and configuration; confirm the exact behavior for the model and endpoint in use.
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OpenAI function calling
OpenAI recommends enabling strict mode for function calling. In its documented strict mode, each object must set additionalProperties to false, and all properties must be marked required; nullable types can represent values that are optional in practice. The guide says Responses attempts strict normalization when strict is omitted and falls back to best-effort non-strict calling if the schema cannot be made compatible. Chat Completions remains non-strict by default. These are documented platform behaviors, not universal rules for other APIs; check the current OpenAI function-calling documentation against your API surface and request configuration.
Anthropic tool validation
Anthropic’s tool reference documents a strict property for validating tool names and inputs for supported tool types, while listing exceptions for MCP, computer, and browser toolsets. Do not assume the same setting or guarantee applies to every tool category. Check the Anthropic tool reference for the exact type and API behavior you use.
SDK validation is configuration-specific
The OpenAI Agents SDK documents that supplying validation schemas enables strict mode by default and that its strict: false option enables fuzzy matching. That is an SDK-specific option, not a general function-calling standard. If using it, make a deliberate decision about whether fuzzy matching is appropriate at that boundary; it should not be mistaken for deterministic exact resolution.
How the controls compare
| Control | What it checks or provides | What to verify | What it does not guarantee |
|---|---|---|---|
| Application closed-world registry lookup | Whether the emitted name maps to an active registered tool | Canonical names, registry snapshot/version, aliases, unknown-name behavior, audit trail | That an existing call is authorized or semantically correct |
| Provider strict tool schema | Whether a call conforms to the declared name/input contract where supported | API surface, supported tool types and schema subset, strict default, rejection or fallback behavior | Uniform behavior across platforms, or resource-level authorization |
| SDK validation and guardrails | Input/output checks around handler execution | Validation timing, error shape, resource-aware authorization, approval support | Authorization merely because a tool was exposed for a request |
| Central agent or tool registry | Discovery and governance of registered components | Runtime coverage, automatic versus manual registration, policy integration, versioning | That every runtime call is authorized or uses a current definition |
| Deterministic schema compilation | How tool contracts are represented to a model | Model and catalog size, token use, accuracy under the benchmark conditions | Name existence or authorization by itself |
When comparing implementations, examine the source of truth for active tools, name and alias rules, snapshot consistency, schema coverage, unknown-name handling, resource-level security, approval and side-effect controls, recovery behavior, call/result correlation, telemetry, and provider dependence. A registry catalog may help govern components without automatically enforcing every runtime decision. Google Cloud’s Agent Registry documentation, for example, distinguishes agents, MCP servers, endpoints, and skills, and describes automatic and manual registration for different resources; see its Agent Registry data model.
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What tool-hallucination studies establish
The 2026 preprint “Closed-World Resolution Against Tool Hallucination in LLM Agents” proposes a training-free “Resolution Rung”: check registry membership and then check the signature before downstream gating. It reports 322 tool hallucinations across ten hosted models and two invocation surfaces, and 154 on its live MCP surface. These are the authors’ benchmark counts, not estimates of how often production agents hallucinate. The paper also describes a residual class in which borrowed arguments are indistinguishable from a valid call under schema checking. Consequently, a matching name and valid shape cannot prove intent or permission.
A separate 2026 preprint, “TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments,” studies transforming JSON schemas into structured text. Its abstract reports benchmark improvements and token savings, but schema representation is a different problem from checking that an emitted name exists in the application registry. Treat its reported results as author-reported benchmark findings, not as independent confirmation that schema compilation resolves hallucinated names.
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