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continue_interaction

Extend a previous agent interaction by adding new prompts, images, or function results to the same environment.

Instructions

Continue an existing interaction turn with new prompt, images, or function results.

Args: previous_interaction_id: ID of the interaction turn to continue. environment_id: Sandbox environment ID returned from previous turn. prompt: Optional follow-up prompt text. images: Optional follow-up base64 images. function_results: Optional list of function_result dicts: [{"name": "...", "call_id": "...", "result": {...}}]. agent: Agent identifier. system_instruction: Optional behavior/persona override for this turn.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNoantigravity-preview-05-2026
imagesNo
promptNo
environment_idYes
function_resultsNo
system_instructionNo
previous_interaction_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description carries full burden but only lists parameters and their types. It fails to disclose side effects (e.g., state mutation), authentication needs, rate limits, or return behavior. The agent cannot assess safety or consequences.

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 concise and well-structured: a one-line summary followed by a bullet-like arg list. Every sentence adds value, with no redundancy or fluff. It front-loads the purpose.

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

Completeness3/5

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

Given the complexity (7 params, no annotations, no output schema description), the description covers purpose and parameters adequately but lacks usage guidelines and behavioral transparency. An output schema exists, so return values are not required, but more context on when to use this tool vs siblings would improve completeness.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It defines all 7 parameters with clear, concise explanations (e.g., 'Optional list of function_result dicts: [{"name": ...}]'). This adds significant meaning beyond the schema's type-only definitions.

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

Purpose5/5

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

The description clearly states the tool continues an existing interaction turn with new inputs, using specific verb 'continue' and resource 'interaction turn'. It distinguishes from siblings like 'create_interaction' (which starts new) and 'submit_function_result' (which is a subcomponent).

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?

The description implies usage for continuing interactions but lacks explicit when-to-use or when-not-to-use guidance. No mention of alternatives or prerequisites, leaving the agent to infer context from the tool name and sibling list.

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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