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eyalbarash
by eyalbarash

cigchat_flow_update_ai_agent_provider

Update the AI agent provider and model for a flow. Provide the flow's namespace or exact name, then set the desired provider and model to reconfigure its AI behavior.

Instructions

update Ai Agent provider and model, available ai_provider: openai, openai-responses, deepseek, xai, xai-responses, claude, gemini, groq, ainvented [POST /flow/update-ai-agent-provider] Requires the "Manage Flow" scope on the token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ai_modelNoExample: gpt-4o-mini
max_tokensNoExample: 500
ai_agent_nsYesAccepts either the ai_agent namespace id or the ai_agent's exact name — the server resolves names automatically. Example: f123ag456
ai_providerNoOne of: openai, openai-responses, deepseek, xai, xai-responses, claude, gemini, groq, ainvented. Example: openai
Behavior2/5

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

With no annotations provided, the description must carry the full transparency burden. It does disclose the HTTP endpoint and the required 'Manage Flow' scope, but it does not describe side effects, whether partial updates preserve existing settings, or error behavior, which is a significant gap for a mutation tool.

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 sentence that front-loads the action, includes the provider list, endpoint, and scope without any filler. It is concise, efficient, and every piece of information earns its place.

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?

The description, combined with the well-covered schema, provides sufficient context for a straightforward update operation: it states the action, endpoint, required scope, and valid providers. However, it omits details about partial update behavior and return values, making it adequate but not rich.

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?

The input schema has 100% coverage with descriptions and examples for every parameter. The description merely repeats the provider list that already exists in the schema, adding no extra semantic meaning beyond what is already structured, so the baseline of 3 is appropriate.

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 uses a specific verb ('update') and resource ('Ai Agent provider and model'), clearly stating what the tool does. It also enumerates the available provider values, which helps distinguish this tool from sibling tools like flow_update_ai_task_provider or settings_set_default_ai_provider.

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 when to use it (to update an AI agent's provider/model) and lists the valid providers, along with the required 'Manage Flow' scope. However, it does not explicitly state when not to use it, nor does it compare against alternative sibling tools.

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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