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orcarouter_chat

Send a single-turn chat request to OrcaRouter and get the assistant's response. Supports model fallback chains, direct provider calls, and automatic max_completion_tokens for reasoning models.

Instructions

Send a single-turn chat request to OrcaRouter and return the assistant's response text. Default model is the workspace's auto-router. Use orcarouter/<name> for other routers or <provider>/<model> for direct calls. For OpenAI reasoning models (gpt-5/o1/o3/...), max_tokens is automatically routed to max_completion_tokens at the wire level. The optional models array sets a fallback chain — the primary model is tried first, then each entry on failure (5 entries total max, including the primary). Errors are returned as text content with isError:true; common cases include missing API key, rate limits, and upstream provider outages. Requires ORCAROUTER_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel to call. Defaults to `orcarouter/auto` — your workspace's seeded auto-router. Use `orcarouter/<name>` for other workspace routers, or `<provider>/<model>` for direct upstream selection (e.g. `openai/gpt-4o-mini`, `anthropic/claude-haiku-4.5`).orcarouter/auto
promptYesUser message to send (single-turn).
system_promptNoOptional system prompt prepended to the conversation.
max_tokensNoMaximum tokens to generate (default 10000). Automatically translated to max_completion_tokens for OpenAI reasoning models.
temperatureNoSampling temperature (default 0.7).
modelsNoOptional fallback chain. Models are tried in order if the primary fails. Max 5 entries including the primary.
Behavior5/5

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

Beyond annotations (no readonly, not destructive), the description details automatic max_tokens routing for reasoning models, fallback chain behavior, error representation (isError:true), common failure cases, and required API key. This enriches agent understanding of side effects and operational constraints.

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 paragraph of 5 sentences, front-loaded with the primary purpose. Every sentence adds essential information (model routing, fallback, error handling, auth) without redundancy or fluff.

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

Completeness5/5

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

Given no output schema, the description covers input usage, error handling, authentication requirements, and behavioral nuances. The sibling tools are all list/info tools, so this tool's role is clearly the primary action tool. No gaps remain.

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 coverage is 100% with descriptions for all 6 parameters. The description adds value beyond schema by explaining the automatic max_completion_token routing for reasoning models and confirming the fallback chain behavior, which is not fully detailed in the schema's parameter descriptions.

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 'Send a single-turn chat request to OrcaRouter and return the assistant's response text,' specifying verb (send), resource (chat request), and outcome. It distinguishes from siblings (model_card, models_list, providers_list) which are information retrieval tools, not action tools.

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 explains when to use default model vs. other routers or direct providers, the fallback chain, error handling, and authentication. It lacks explicit 'when not to use' but the sibling tools are clearly different, so usage context is well established.

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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