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get_models

List available models with configuration options for workspace pipelines and indexes. Filter by provider, model name, or connection status to identify suitable models.

Instructions

Lists the models including their configuration options available for use in a workspace's pipelines and indexes.

This includes predefined models offered by deepset as well as custom models configured at the workspace or organization level. Use this tool to discover which model names and providers can be used, which configuration options are available, and which default configuration is offered when configuring chat generators. :param limit: Maximum number of models to return per page. :param page_number: The page to fetch, starting at 1. :param connected: If set, only return models for which the workspace does (True) or does not (False) have a working integration configured. :param provider: If set, only return models from this provider. Accepts a well-known ModelProvider value (e.g. 'openai', 'anthropic', 'gemini', 'aws-bedrock') or any other provider name as a plain string. Case-insensitive, exact match. :param model: If set, only return models whose configured model name contains this value (e.g. 'gpt-4o'). Case-insensitive, substring match. :returns: A page of models including their configuration options or an error message.

The output is automatically stored and can be referenced in other functions. Returns a formatted preview with an object ID (e.g., @obj_123). Use the object store tools in combination with the object ID to view nested properties of the object. Use the returned object ID to pass this result to other functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
modelNo
providerNo
connectedNo
page_numberNo
Behavior4/5

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

With no annotations, the description carries full burden and provides good transparency: it states the tool lists models (read-only), returns a page of models with an object ID, explains output storage and how to access nested properties, and mentions error messages. It does not cover rate limits or page size limits but is otherwise thorough.

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

Conciseness4/5

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

The description is well-structured with a clear purpose paragraph, parameter documentation, and output handling. It is front-loaded and each sentence adds value, though some redundancy exists (e.g., repeating 'configuration options'). Overall, it is appropriately sized.

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 5 parameters, no required fields, no output schema, and no annotations, the description is highly complete. It explains all parameters, output format (object ID), how to use the object store, error messages, and the scope (workspace/organization level). No important details are missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates by providing detailed explanations for all 5 parameters: limit, page_number, connected, provider (with well-known values and case-insensitivity), and model (with substring match). Each parameter's behavior is clearly explained.

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 lists models including configuration options, covering predefined and custom models, and specifies its use case for discovering model names, providers, and default configurations for chat generators. It distinguishes itself from sibling tools that handle indexes, pipelines, or workspaces.

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 explicitly states when to use the tool ('Use this tool to discover...'), providing clear context. However, it does not explicitly mention when not to use it or alternatives, though no competing sibling tool exists.

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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