Skip to main content
Glama

pay_invoice

Process Bitcoin Lightning payments by submitting invoice details to complete transactions through the NWC MCP Server.

Instructions

Pay a lightning invoice

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
invoiceYesThe lightning invoice to pay
amountNoOptional amount in millisats to pay a zero-amount invoice
metadataNoOptional metadata to include with the payment

Implementation Reference

  • The asynchronous handler function that executes the pay_invoice tool logic: calls client.payInvoice with the input parameters and returns the result as a text content block with JSON stringified response.
      async (params) => {
        const result = await client.payInvoice({
          invoice: params.invoice,
          amount: params.amount || undefined,
          metadata: params.metadata || undefined,
        });
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      }
    );
  • Zod schema for input parameters of the pay_invoice tool: required 'invoice' string, optional 'amount' number in millisats, optional 'metadata' passthrough object.
    {
      invoice: z.string().describe("The lightning invoice to pay"),
      amount: z
        .number()
        .describe("Optional amount in millisats to pay a zero-amount invoice")
        .nullish(),
      metadata: z
        .object({})
        .passthrough()
        .describe("Optional metadata to include with the payment")
        .nullish(),
    },
  • The registerPayInvoiceTool function exports the registration logic, calling server.tool to register the 'pay_invoice' tool with its schema and handler.
    export function registerPayInvoiceTool(
      server: McpServer,
      client: nwc.NWCClient
    ) {
      server.tool(
        "pay_invoice",
        "Pay a lightning invoice",
        {
          invoice: z.string().describe("The lightning invoice to pay"),
          amount: z
            .number()
            .describe("Optional amount in millisats to pay a zero-amount invoice")
            .nullish(),
          metadata: z
            .object({})
            .passthrough()
            .describe("Optional metadata to include with the payment")
            .nullish(),
        },
        async (params) => {
          const result = await client.payInvoice({
            invoice: params.invoice,
            amount: params.amount || undefined,
            metadata: params.metadata || undefined,
          });
          return {
            content: [
              {
                type: "text",
                text: JSON.stringify(result, null, 2),
              },
            ],
          };
        }
      );
    }
  • Invocation of registerPayInvoiceTool within createMCPServer to register the pay_invoice tool on the MCP server instance.
    registerPayInvoiceTool(server, client);
  • src/mcp_server.ts:7-7 (registration)
    Import of the registerPayInvoiceTool function from the pay_invoice module.
    import { registerPayInvoiceTool } from "./tools/pay_invoice.js";
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but lacks critical details like whether this is a destructive operation, what permissions are needed, potential side effects (e.g., fund transfer), or error handling, leaving significant gaps for a payment 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, efficient sentence with zero waste, front-loading the core action. It's appropriately sized for the tool's complexity, making it easy to parse quickly.

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 complexity (a payment operation with financial implications), no annotations, and no output schema, the description is incomplete. It fails to address behavioral aspects, return values, or error cases, leaving the agent with insufficient context for safe and effective use.

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?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond the schema, such as explaining the 'amount' parameter's role with zero-amount invoices or 'metadata' usage. Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('pay') and resource ('a lightning invoice'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'make_invoice' or 'lookup_invoice' beyond the verb, which keeps it from a perfect 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. It doesn't mention prerequisites, such as having sufficient balance, or clarify scenarios like paying zero-amount invoices with the 'amount' parameter, leaving usage context ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/getAlby/nwc-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server