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farebox-mcp

farebox-mcp

by fareboxfun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

farebox-mcp

Farebox MCP Server — connect Claude Desktop, Cursor, or any MCP-compatible client to 36+ frontier LLMs. Payments happen automatically per token in USDC on Solana — no accounts, no invoices.

npm version License: MIT

Install

npx farebox-mcp

No global install needed. Runs directly from the npm registry.

Related MCP server: farebox-mcp

Quick setup

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "farebox": {
      "command": "npx",
      "args": ["-y", "farebox-mcp"],
      "env": {
        "FAREBOX_API_KEY": "sk-fbx-your-key-here"
      }
    }
  }
}

Cursor

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "farebox": {
      "command": "npx",
      "args": ["-y", "farebox-mcp"],
      "env": {
        "FAREBOX_API_KEY": "sk-fbx-your-key-here"
      }
    }
  }
}

Get your API key at farebox.fun/dashboard.

Keyless mode (x402)

No API key required — your agent pays per request autonomously in USDC on Solana:

{
  "mcpServers": {
    "farebox": {
      "command": "npx",
      "args": ["-y", "farebox-mcp"],
      "env": {
        "FAREBOX_SOLANA_PRIVATE_KEY": "your-base58-private-key",
        "FAREBOX_MODE": "x402"
      }
    }
  }
}

Available tools

Once connected, your agent has access to:

  • 36+ frontier models — GPT-4o, Claude Sonnet, Gemini Flash, Llama, DeepSeek, Mistral, and more

  • Community skills — summarize, translate, code-review, explain, sentiment, extract-data, draft-email, fix-grammar

All models and skills appear as individual tools in your MCP client automatically.

Environment variables

Variable

Description

FAREBOX_API_KEY

API key from farebox.fun/dashboard

FAREBOX_SOLANA_PRIVATE_KEY

Base58 Solana private key (x402 keyless mode)

FAREBOX_MODE

apikey (default) or x402

FAREBOX_BASE_URL

Override API base URL (optional)

Pricing

Pay per token in USDC — no minimum, no subscription.

Model

Input

Output

llama-4-scout

$0.11 / 1M tokens

$0.34 / 1M tokens

gpt-4o-mini

$0.15 / 1M tokens

$0.60 / 1M tokens

claude-sonnet-4-5

$3.00 / 1M tokens

$15.00 / 1M tokens

gpt-4o

$2.50 / 1M tokens

$10.00 / 1M tokens

Full model list: farebox.fun/models

Links

License

MIT

Available Tools

5 tools
call_skillB

Call a Farebox built-in skill (summarize, translate, code-review, explain, sentiment, extract-data). Faster than crafting a prompt from scratch.

Inputs: input (required), skill (required), model.

chatB

Send a chat completion to any Farebox model. Returns the assistant's reply.

Inputs: model (required), messages (required), max_tokens, temperature.

get_balanceA

Get the current Farebox account balance and spending totals in USD.

Inputs: no named parameters.

get_usageC

Get recent usage statistics from your Farebox account.

Inputs: days.

list_modelsA

List all available Farebox models with pricing. Returns an array of model objects.

Inputs: no named parameters.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.5/5.0

    Scored across 5 tools

    Disambiguation4/5

    The tools are mostly distinct: list_models, chat, call_skill, get_balance, and get_usage each target a different concern (model discovery, inference, skills, billing, usage). However, get_balance and get_usage both deal with account-level billing/statistics and could be slightly ambiguous for an agent deciding which to call.

    Naming Consistency4/5

    Tool names follow a consistent verb_noun pattern: list_models, call_skill, get_balance, get_usage. The only deviation is 'chat', which uses a bare noun-verb form rather than a verb_noun structure (e.g., send_chat or chat_completion), breaking pattern slightly.

    Tool Count5/5

    Five tools is a well-scoped, focused set for an LLM API server. Each tool serves a clear purpose: discovery, inference, specialized skills, and account management. Every tool earns its place without redundancy.

    Completeness4/5

    The core workflow (discover models, chat, call skills) is well covered, and account management is present. Minor gaps exist: no explicit skill-list/discovery tool (skills are only implied via call_skill), and no streaming or multi-turn session management, but these are reasonable to omit for a lightweight connector.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

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