Enables AI agents to use NIM models for tool calling, allowing the model to autonomously choose functions and arguments.
Use NIM models with tool/function calling capabilities. The model will decide which function to call and with what arguments.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| tools | Yes | ||
| messages | Yes | ||
| max_tokens | No | ||
| temperature | No | ||
| tool_choice | No |
Changes observed during successful MCP inspections.
v2.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does reveal that the model decides which function to call and with what arguments, which is important, but it omits details about the response format, whether tools are executed, pagination, rate limits, or the tool_choice behavior. This is minimal disclosure for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences with no filler or redundancy. It is efficiently front-loaded, but it might be too sparse to be considered excellently structured, hence a 4 rather than a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, 2 required, no output schema, no annotations), the description is severely incomplete. It only mentions the high-level capability and gives no information about required inputs, return values, or edge cases. This is far below the minimum viable description for such a tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 6 parameters with 0% description coverage, so the description must compensate. It fails to do so: it does not explain the purpose of 'model', 'tools', 'messages', 'max_tokens', 'temperature', or 'tool_choice'. The only indirect hint is 'function calling' and 'with what arguments,' which vaguely relates to 'tools' but adds no concrete parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool uses NIM models with tool/function calling capabilities, which is a specific verb+resource+capability. It clearly indicates the tool's role but does not explicitly distinguish it from sibling tools like chat_completion, though the function-calling focus provides some differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use NIM models with tool/function calling capabilities' but gives no guidance on when to choose this tool over alternatives like chat_completion or text_generation. There are no explicit exclusions, prerequisites, or recommended scenarios, leaving the decision to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.