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list_agents

Display all AI agents in your Lightning Wallet MCP operator account to manage spending limits and budget controls.

Instructions

List all agents under your operator account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'list_agents' which calls the client's listAgents method.
    case 'list_agents': {
      ListAgentsSchema.parse(args);
      const result = await session.requireClient().listAgents();
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify({
              success: true,
              agents: result.agents,
              total: result.agents.length,
            }, null, 2),
          },
        ],
      };
    }
  • The actual implementation in the LightningFaucetClient class that makes the API request to the backend.
    async listAgents(): Promise<{
      agents: Array<{
        id: number;
        name: string;
        balance_sats: number;
        is_active: boolean;
      }>;
      rawResponse: ListAgentsResponse;
    }> {
      const result = await this.request<ListAgentsResponse>('list_agents');
    
      const agents = (result.agents || []).map(agent => ({
        id: agent.id,
        name: agent.name,
        balance_sats: agent.balance_sats,
        is_active: agent.is_active,
      }));
    
      return {
        agents,
        rawResponse: result,
      };
    }
  • Input schema definition for the list_agents tool.
    const ListAgentsSchema = z.object({});
  • src/index.ts:440-447 (registration)
    Tool registration definition for 'list_agents' in the server tool list.
      name: 'list_agents',
      description: 'List all agents under your operator account.',
      inputSchema: {
        type: 'object',
        properties: {},
        required: [],
      },
    },
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists agents but doesn't mention any behavioral traits such as whether it's read-only (implied by 'list'), potential rate limits, authentication requirements beyond 'your operator account', or the format of the returned data. This leaves significant gaps for an agent to understand how to interact with it effectively.

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, clear sentence that directly states the tool's purpose without any fluff or redundant information. It is front-loaded and efficiently communicates the essential action, making it highly concise and well-structured.

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

Completeness2/5

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimal but adequate for basic understanding. However, it lacks completeness for effective use by an AI agent, as it doesn't cover behavioral aspects like data format, pagination, or error handling. With no output schema and no annotations, more context would be helpful.

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 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied for zero parameters, as it avoids unnecessary information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List all agents') and the resource ('under your operator account'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish itself from sibling tools like 'get_budget_status' or 'get_transactions' that might also involve agent-related information, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. For example, it doesn't mention if this is for retrieving a full list versus filtered results or when to prefer other agent-related tools like 'get_budget_status'. There's only an implied context of needing to see all agents, with no explicit usage instructions.

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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