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Create Lyzr Agent

lyzr_create_agent

Create a Lyzr agent with name, role, goal, and instructions. Returns an agent_id for use with lyzr_chat.

Instructions

Create a new Lyzr agent. Returns the new agent_id to use with lyzr_chat.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesWhat the agent should accomplish
nameYesAgent name
roleYesThe agent's role/persona
modelNoModel name, e.g. gpt-4o-mini, gpt-4o, claude-sonnet-4-5gpt-4o-mini
providerNoLLM provider. One of: openai, anthropic, google, gemini, groq, perplexity, aws-bedrock, bedrock, awsopenai
descriptionNoOptional agent description
temperatureNoSampling temperature (0-2, default 0.7)
instructionsYesSystem instructions that steer the agent
Behavior3/5

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

Annotations already indicate this is a write operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds the useful detail that it returns the new agent_id, but it does not disclose other potential side effects, such as uniqueness constraints, persistence guarantees, or permissions required. That extra return-value context is helpful, but minimal, so a 3 is appropriate.

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 only two sentences: one stating the primary action and one stating the result/return value. Every word earns its place, and the most important information is front-loaded. There is no redundant or filler content.

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?

For a create tool with 8 parameters and no output schema, the description covers the key elements: what it does and what it returns (agent_id). It also ties into the broader workflow via 'to use with lyzr_chat.' However, it doesn't mention that name/role/goal/instructions are required (though the schema covers that) or any uniqueness/conflict behavior. Still, given the rich schema and annotations, it is reasonably complete for the agent to select and invoke the tool.

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 provides 100% coverage with descriptions for all 8 parameters, so the baseline is 3. The description adds no parameter-specific details beyond what the schema already offers. It does not name or explain any of the parameters, so it neither adds nor detracts from the schema.

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 'Create a new Lyzr agent' with a specific verb and resource, and it explicitly distinguishes this from sibling tools like lyzr_update_agent, lyzr_delete_agent, and lyzr_list_agents. It also mentions the return value (agent_id) and its intended use with lyzr_chat, which further clarifies the tool's purpose.

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 phrase 'to use with lyzr_chat' provides clear context that this tool is the prerequisite for creating an agent before chatting. However, it does not explicitly mention alternatives or exclusions (e.g., when to prefer lyzr_create_a2a_agent or lyzr_update_agent), so it stops short of full when-to-use vs when-not-to-use guidance.

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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