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Elicit missing tool args

elicit_missing_args
Read-only

When a tool call lacks required args, generate proposals for them using schema and LLM based on user context.

Instructions

Use the schema + LLM to propose values for a tool call's missing required args.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoNatural-language context the user gave (a chat message, prompt, etc.).
tool_nameYesRegistered tdmcp tool name, e.g. 'create_audio_reactive'.
max_fieldsNoCap on how many missing required fields to elicit in one call.
temperatureNoSampling temperature for elicitation. Low by default for determinism.
partial_argsNoArgs already known. Missing required fields will be elicited.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
filledYesElicited values keyed by field name. `null` when LLM declined/unavailable.
sourceYes'llm' if the model answered, 'offline' if no LLM, 'none-needed' if nothing missing.
missingYesRequired fields that were still missing after elicitation (filled[k] === null).
warningsYes
tool_nameYes
proposed_argsYespartial_args merged with non-null filled, validated against the tool schema.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint, so the description does not need to restate safety. It adds the method context ('use the schema + LLM') and clarifies it 'proposes' rather than executes, but it does not describe output shape, failure behavior, or limits, leaving behavioral detail to the schema and output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that communicates the core purpose with no wasted words. It is direct and efficient, earning a high score on conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich schema, clear annotations, and presence of an output schema, the description is sufficient to understand the tool's primary role. It could be slightly more complete by stating it does not execute the target tool, but 'propose values' already implies this, so the overall description is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline applies. The description itself does not add parameter-level nuance; all parameter meaning is carried by the input schema. It neither harms nor significantly augments the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: using the schema and an LLM to propose values for missing required arguments in a tool call. It identifies a specific verb ('propose') and resource ('missing required args'), but it does not explicitly contrast it with sibling tools, so it stops short of full differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use this when a tool call has missing required args. There is no explicit 'when to use' or 'when not to use' guidance, nor mention of alternative approaches, so the guidance is minimally viable but not robust.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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