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Leximo-AI

leximo-ai-call-assistant-mcp-server

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by Leximo-AI

list_agents

Retrieve all available AI calling agents to select one for an assignment. Use this before creating an assignment to identify the agent ID required.

Instructions

List all available AI calling agents. IMPORTANT: Always call this BEFORE creating an assignment. Present the returned agents to the user so they can choose which one should make the call, or recommend one based on the user's task. The agent's ID is required for create_assignment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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 implicitly indicates a read-only operation (listing) but does not explicitly state that no modifications occur. The context of listing is clear, but additional transparency about safety could improve it.

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?

Three sentences with no wasted words. The critical usage guidance (call before create_assignment) is emphasized and front-loaded, making it easy to parse.

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 absence of an output schema, the description does not detail the structure of returned agents. However, it sufficiently conveys the purpose and how to use the output (e.g., agent ID for create_assignment). A slight addition of common fields would make it more complete.

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?

The tool has no parameters, so schema coverage is 100%. The description appropriately focuses on usage without needing parameter details, meeting the baseline for zero-parameter tools.

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 all available AI calling agents, using a specific verb and resource. It distinguishes itself from siblings like get_agent (which retrieves a single agent) and other assignment tools.

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

Usage Guidelines5/5

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

Explicitly instructs to call this before create_assignment, advises to present agents to the user or recommend one, and explains the importance of the agent ID for the next step.

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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