lightningprox-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| chatB | Send a message to an AI model via LightningProx. Pay per request with a Lightning spend token. Supports 19 models from Anthropic, OpenAI, Together.ai, Mistral, and Google. |
| list_modelsB | List all AI models available through LightningProx. Returns model IDs, names, providers, and pricing. 19 models across Anthropic, OpenAI, Together.ai, Mistral, and Google. |
| get_balanceB | Check the remaining balance on a LightningProx spend token. Returns balance in sats. |
| generate_invoiceA | Generate a Bitcoin Lightning invoice to top up a LightningProx spend token. Returns a BOLT11 payment request and charge ID. Pay the invoice with any Lightning wallet. |
| check_paymentA | Check if a Lightning invoice has been paid and retrieve the spend token. Poll this after generate_invoice until the payment is confirmed. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: chat sends messages, check_payment verifies invoices, generate_invoice creates payment requests, get_balance checks token balances, and list_models shows available models. There is no functional overlap between these operations.
Four tools follow a consistent verb_noun pattern (check_payment, generate_invoice, get_balance, list_models), while 'chat' is a standalone verb. This minor deviation from the pattern is still readable and intuitive.
With 5 tools, this server is well-scoped for its purpose of AI model access via Lightning payments. Each tool earns its place by covering essential operations: model listing, chat interaction, payment handling, and balance management.
The toolset provides complete coverage for the domain: list_models for discovery, chat for core interaction, generate_invoice and check_payment for payment flow, and get_balance for token management. No obvious gaps exist for the stated purpose.