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rivenai
by rivenai

Riven Chat

riven_chat

Send a single prompt to a Riven chat model and receive the completion text. For quick, single-turn questions that require no agentic research.

Instructions

Send a single prompt to a Riven chat model and return the completion text. Use for quick, single-turn questions that don't need agentic research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel id to use, e.g. 'rvn-assistant-v2' (flagship alias), 'glm-5.2', or 'qwen3.6-35b'. Defaults to 'rvn-assistant-v2' if omitted.
promptYesThe user prompt to send to the model.
systemNoOptional system prompt to steer the model.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states that the tool returns completion text, which is minimal. It does not disclose potential behavioral traits such as rate limits, authentication requirements, or whether it uses conversation history. For a simple generation call, this is adequate but not rich.

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 two sentences long, with no redundant information. The first sentence clearly defines the function and output, the second provides usage guidance. Every word serves a purpose.

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 tool's simplicity (3 parameters, no output schema, no nested objects), the description covers purpose, parameters, and usage. It explains that the output is completion text. While more detail on token limits or error handling could be added, the current description is sufficient for an agent to understand and use the tool correctly.

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 coverage is 100%, baseline is 3. The description adds value by providing example model IDs (e.g., 'rvn-assistant-v2', 'glm-5.2') and noting the default. However, the prompt and system descriptions largely reiterate the schema. Overall, some added detail but not substantial.

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 explicitly states the action (send a single prompt), the resource (Riven chat model), and the result (return completion text). It also clearly distinguishes from sibling tools by indicating it's for non-agentic, single-turn questions, contrasting with 'riven_research' which presumably handles agentic research.

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

Usage Guidelines4/5

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

The description provides a clear usage context: 'quick, single-turn questions that don't need agentic research.' This implicitly tells the agent when to use this tool vs. riven_research. However, it does not explicitly mention other siblings like riven_council or riven_usage, which might serve different purposes.

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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