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 an unknown name; validate arguments against the matched tool’s contract; then check authorization and any required approval before dispatch. Provider-side schema enforcement helps, but it does not replace these application-side gates.
What deterministic name resolution does—and does not do
A tool call is a request for the application to act. The model returns a structured call; the application selects and executes the corresponding function, then returns a result associated with that call. In OpenAI’s documented flow, the result references the initiating call with its call_id (OpenAI function calling).
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Two problems are easy to conflate:
- Selection: Does a tool available to the model suit the user’s request?
- Resolution: Does the emitted name bind to exactly one tool in the active registry, and do the arguments satisfy that tool’s declared contract?
A model can choose the wrong real tool; resolution alone does not decide whether that choice makes sense. But if it emits a misspelled, stale, or invented name, exact lookup catches the unbindable call before a handler runs. The application should not guess what an unknown name “probably meant.”
There are three separate gates: existence (a matching registered tool), contract (arguments conform to its signature), and permission (this caller may perform this operation on this resource). Passing one does not imply passing the next.
Build resolution around the active registry
Keep a canonical registry keyed by tool name. Each entry should connect the public name exposed to the model to one implementation, one input schema or signature, and an explicit version. For each request or turn, retain the registry snapshot used to construct the model’s available-tool list. Validate against that same snapshot, not a catalog that may have changed since the model received the definitions.
This registry-and-signature design is an application architecture, not a universal protocol requirement. Its purpose is to give the runtime one trusted source of truth for what can be dispatched. Agent catalogs can help with discovery and governance: for example, Google Cloud distinguishes agents, MCP servers, endpoints, and skills in its registry data model, and describes both automatic and manual registration paths. A catalog by itself does not prove a runtime call is current or authorized (Google Cloud Agent Registry data model).
Decide alias behavior explicitly
If backward compatibility requires aliases, declare them in the registry and map each alias to exactly one canonical entry. Reject ambiguous aliases. Do not add fuzzy matching as an invisible fallback: it can send a call to a different implementation than the one the model named. The reviewed platform sources do not establish a cross-platform alias standard.
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- Parse the call envelope. Confirm the payload is well-formed and preserve its call identifier. Malformed encoding is a distinct failure from an unknown tool name.
- Resolve the name exactly. Look up the returned name in the active registry snapshot. If there is no match, stop. Return a bounded error or ask the model to select from the tools actually available; do not dispatch.
- Validate against the matched signature. Check required fields, types, and unexpected properties. Parse the payload into a validated representation, and pass that representation—not unchecked model text—to the handler.
- Authorize the caller and target. Check identity, tenant, resource, and operation in application-controlled code or a trusted guardrail. A valid schema is not permission to access the resource named in the arguments.
- Apply approval or policy gates. Require confirmation where the product’s action policy calls for it, especially before consequential side effects.
- Dispatch and correlate the result. Execute only after the previous checks pass. Return the result associated with the initiating call identifier when required by the platform.
For example, suppose the active registry contains get_weather with a required string field, location. If the model emits get_weathr, exact lookup fails and no handler runs. If it names get_weather but omits location or adds a field the contract disallows, validation fails. If the call is structurally valid but targets a location the user cannot query, authorization blocks it.
Illustrative dispatch boundary
This language-agnostic sketch shows the order of checks; it is not a platform-specific API:
call = parse_tool_call(response)
tool = active_registry.exact_lookup(call.name)
if tool is missing:
reject("unknown_tool")
args = tool.schema.validate(call.arguments)
if args is invalid:
reject("invalid_arguments")
if not authorize(identity, tool, args):
reject("not_authorized")
if requires_approval(tool, args) and not approved(call):
reject("approval_required")
result = tool.handler(args)
return result_for(call.id, bounded(result))Keep this boundary in application-controlled code even when the provider enforces structured output. Provider constraints can reduce malformed calls; the application still binds names to its own implementations and applies its own access policy.
Use provider schemas as an additional control
Strict schema features can enforce parts of a tool contract, but their requirements and coverage depend on the API surface and tool type.
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OpenAI function calling
OpenAI recommends enabling strict mode for function calling. Its documented strict schema requirements include setting additionalProperties to false on every object and marking every property as required; nullable types can represent values that are logically optional. The guide says Responses attempts strict normalization when strict mode is omitted and can fall back to best-effort non-strict calling when a schema is incompatible. Chat Completions remains non-strict by default in the documented behavior. Check the current supported schema subset and the configuration for the model and API surface you use (OpenAI function calling).
Anthropic tool validation
Anthropic’s tool reference documents a strict property for schema validation of tool names and inputs on supported user-defined tools, and lists exceptions for MCP, computer, and browser toolsets. Do not assume the same guarantee applies across those tool types or across providers; check the exact API and tool configuration (Anthropic tool reference).
SDK validation and visibility
The OpenAI Agents SDK documents that validation schemas automatically enable strict mode by default and includes an SDK-specific strict: false fuzzy-matching option. Treat that as SDK behavior, not a general provider setting. The SDK also cautions that request-scoped tool visibility does not replace authorization based on the arguments or target resource; enforce those checks in execution or guardrails (OpenAI Agents SDK tools).
Microsoft’s function-calling guidance says to “Treat tool arguments and tool outputs as untrusted input.” Validate and sanitize values, use least-privilege credentials, avoid unintended side effects, and return only information the model needs (Microsoft Foundry function calling).
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A schema can show that account_id is a string; it cannot show that the current user may access that account. Nor does a valid call establish that its effect is safe or appropriate. Perform resource-level authorization after resolving the tool and validating its arguments, but before any operation with side effects.
- Derive identity and tenant context from trusted application state, not from model-supplied arguments.
- Check access to the specific target resource and requested operation.
- Use least-privilege credentials and avoid returning secrets or unnecessary data in tool results.
- Require human approval when the action policy requires it; approval is an additional gate, not a substitute for authorization.
- Validate and sanitize tool outputs too, particularly before feeding them back into the model or another system.
Make failures useful and traceable
Do not collapse every rejection into a generic “tool failed” event. Record the call identifier, resolved canonical name when there is one, registry or schema version, validation result, authorization or approval outcome, and handler result. Keep errors returned to the model bounded and non-sensitive: they should support recovery without exposing registry internals, credentials, or protected data.
Track at least these outcomes separately:
- Unknown tool name
- Malformed argument encoding
- Schema or signature mismatch
- Authorization denied
- Approval required or denied
- Timeout
- Handler failure
- Successful execution
In Microsoft’s troubleshooting guidance, missing tools may relate to an absent agent definition or poor naming, invalid JSON to schema mismatch or incorrect model output, and wrong parameters to ambiguous descriptions. Separating those cases helps distinguish a registry/configuration issue from a model-output or handler issue (Microsoft Foundry function calling).
How the approaches fit together
| Control | What it checks | What to verify | What it does not establish |
|---|---|---|---|
| Application closed-world lookup | Whether the emitted name maps to an active registered tool. The closed-world resolution proposal describes registry membership plus a signature check (2026 preprint). | Exact matching, registry snapshot/version, alias policy, unknown-name behavior, audit trail. | Authorization or semantic correctness of a valid call. |
| Provider strict tool schema | Whether the name and inputs conform to the tool contract as supported by that API. | API surface, tool type, schema subset, strict configuration, and rejection or fallback behavior; see OpenAI and Anthropic. | Uniform guarantees across providers, surfaces, and tool types; resource permission. |
| SDK validation and guardrails | Input and output checks around handler execution. | Validation timing, error shape, resource-aware authorization, and approval support; see the OpenAI Agents SDK. | Authorization merely because a tool was exposed for a request. |
| Central agent/tool registry | Discovery and governance of registered components. | Runtime coverage, registration path, policy integration, and versioning; see Google Cloud’s data model. | That each live call is current or authorized. |
| Deterministic schema compilation | How schemas are represented to a model. | Model and catalog size, token use, and accuracy under benchmark conditions; the cited work is a preprint (TSCG, 2026). | Tool-name existence or authorization by itself. |
When evaluating an implementation, compare its source of truth for active tools, name and alias rules, snapshot consistency, schema coverage, unknown-name behavior, authorization and approval controls, error recovery, call/result correlation, telemetry, and provider dependence. These controls complement one another; none should be mistaken for a complete substitute for the others.
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What recent preprints do—and do not—show
The 2026 preprint “Closed-World Resolution Against Tool Hallucination in LLM Agents” proposes a training-free “Resolution Rung” based on registry membership and signature checks before downstream gating. It reports 322 tool hallucinations across ten hosted models and two invocation surfaces, plus 154 on its live MCP surface. These are the authors’ benchmark counts, not estimates of how often production agents hallucinate. The paper also identifies a residual case: borrowed arguments can be indistinguishable from a valid call under schema checks. Treat the proposal and measurements as preliminary, author-reported findings, not settled consensus (arXiv preprint).
A separate May 2026 preprint, “TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments,” studies converting JSON schemas into structured text. Its abstract reports benchmark improvements and token savings, but that work addresses schema representation and interpretation, not whether an emitted name exists in the active registry. Its figures are author-reported benchmark results pending independent replication (arXiv preprint).
Platform details above were checked on October 4, 2026 UTC. API behavior and supported schema features can change, so verify the documentation for the precise provider, version, surface, and tool type you deploy.
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