Tools & Integration · Data artifact
Tool Schema
Data artifactTools & IntegrationOrchestration & Toolsarc:ToolSchema
A typed, versioned contract describing a tool's parameters, types, constraints, and return values, used by the model to construct calls and by the executor to validate them.
Responsibility. Specifies a tool's input and output contract.
Also known as: Function schema, Schema-driven interface, Function definition with structured JSON output, Tool description metadata, Function description metadata, kernel_function metadata, Input schema, Output schema, Tool description, Tool contract, Formal tool specification, JSON Schema tool definition, Tool documentation, Tool metadata decorator (READ/WRITE), Retriever tool specification
Relationships
configures structural
is read by dependency
- Dynamic Tool Loader Ch3.10
- Parameter Context Grounding Validator Ch3.7
- Parameter Slot Filler Ch3.8
- Prompt Context Builder Ch8.3
- Tool Call Accuracy Evaluator Ch3.8
- Tool Call Schema Validator Ch3.7
- Tool Executor Ch3.6
- Tool Parameter Normalizer Ch3.4 Ch3.6
- Tool Response Plausibility Checker Ch3.7
- Tool Result Transformer Ch2.6 Ch3.7
is evaluated by assurance
is produced by lifecycle
Design guidance
- SHOULD specify precise parameter types and structures rather than vague free-text inputs.
- SHOULD be versioned to maintain backward compatibility as tools evolve.
- MUST describe when the tool applies, its domain, expected input format, return format and examples, since the model selects tools from descriptions.
- MUST describe each function precisely and in context-rich terms; vague descriptions degrade LLM routing accuracy.
- MUST state explicitly when the tool should and should not be used, with example scenarios.
- SHOULD specify types, descriptions, required vs optional parameters with defaults, and constraints (ranges, patterns, enums) for every parameter.
- SHOULD define an output schema with field names, types, units, and descriptions.
- SHOULD use concise, specific snake_case or camelCase names rather than generic names such as fetch or query.
- SHOULD encode chain dependencies as parameters that reference previous tool results.
- SHOULD include comprehensive parameter descriptions with examples of accepted variants (e.g., 'revenue (also accepts: top-line, sales, turnover)').
- SHOULD give tool descriptions explicit use cases that distinguish when each similar tool applies.
- MUST give every tool a unique name, a precise description of what it does and when to use it versus alternatives, a formal parameter schema (types, required/optional, ranges, format patterns, interdependencies), and expected return types and error responses.
- SHOULD include examples of valid parameter values and non-obvious constraints (e.g., 'Region must be a 2-letter US state code').
- MUST declare output schemas of chained tools precisely enough to match the input requirements of the consuming tool (e.g., 'returns: float, dollars without currency symbol').
- SHOULD standardize parameter names for equivalent concepts, return formats across tool families, and error response structures.
- SHOULD be revised when audit logs show parameter errors clustering on specific parameters or frequent confusion between similar tools.
- SHOULD keep tool descriptions concise, covering only essential parameters, and group related tools to reduce function count.
- SHOULD give the retriever tool a name and a description telling the agent when to use it (Ref7.07).
Quantitative guidance
As stated by the sources; verify before use.
- About 44% of simple and 48% of complex queries contain parameter errors stemming from inadequate tool documentation (research cited in Ch3.7).
- Removing usage examples from tool specifications drops tool selection accuracy 5-15%; removing parameter constraints increases parameter error rates 10-20% (Ch3.7 ablation findings).
- 15 tool definitions at ~200 tokens each consume ~3,000 tokens before any logic executes (Ch3.10).
- Loan agent tool descriptions compressed from 2,100 tokens (25% of total) to 600 tokens (Ch3.10).
- Definitions of 47 tools consumed 4,200 input tokens per request in the TikTok testing-agent case (Ch8.3).
Classification
- Patterns
- Schema-driven interfacesContract-first tool designDocumentation-accuracy feedback loop
- Technologies
- JSON SchemaOpenAPIOpenAPI 3.0+
- Quality attributes
- Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Compatibility (ISO/IEC 25010)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Interaction capability (ISO/IEC 25010)
- Risks mitigated
- Argument errorsHallucinated argument namesSilent breakage on API changeIncorrect tool selection from vague descriptionsMissing parametersFormat mismatchesTool selection errors from vague descriptionsParameter format errors from undocumented constraintsCascading failures from output/input schema mismatches between chained toolsTool hallucination caused by unclear or inconsistent documentation
- Frameworks & regulations
- JSON Schema 2020-12
Sources
- Ch1.2: T. Nguyen, "Core Agent Patterns," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.2. ISBN: 9798244538229.
- Ch2.1: T. Nguyen, "Framework Landscape and Selection," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.1. ISBN: 9798244538229.
- Ch2.3: T. Nguyen, "LangChain Sequential Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.3. ISBN: 9798244538229.
- Ch2.5: T. Nguyen, "Semantic Kernel," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.5. ISBN: 9798244538229.
- Ch2.6: T. Nguyen, "Tool Integration and Function Calling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.6. ISBN: 9798244538229.
- Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
- Ch3.6: T. Nguyen, "Trace Analysis and Execution Debugging," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.6. ISBN: 9798244538229.
- Ch3.7: T. Nguyen, "Tool Usage Auditing," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.7. ISBN: 9798244538229.
- Ch3.8: T. Nguyen, "Action Accuracy Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.8. ISBN: 9798244538229.
- Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
- Ch4.7: T. Nguyen, "Scaling Strategies," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.7. ISBN: 9798244538229.
- Ch8.3: T. Nguyen, "Token Economics and Architecture," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.3. ISBN: 9798244538229.
- Ref7.07: E. Li, V. Bellotti, R. Kraus, and R. Kao, "Build a retrieval-augmented generation (RAG) agent with NVIDIA Nemotron," NVIDIA Technical Blog, Sep. 23, 2025. [Online]. Available: https://developer.nvidia.com/blog/build-a-rag-agent-with-nvidia-nemotron/
- Ref7.13: NVIDIA, "Llama Nemotron," NVIDIA NeMo Framework User Guide, v25.09. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/nemo-framework/user-guide/25.09/llms/llama_nemotron.html