farebox-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 |
|---|---|
| list_modelsA | List all available Farebox models with pricing. Returns an array of model objects. |
| chatB | Send a chat completion to any Farebox model. Returns the assistant's reply. |
| call_skillB | Call a Farebox built-in skill (summarize, translate, code-review, explain, sentiment, extract-data). Faster than crafting a prompt from scratch. |
| get_balanceA | Get the current Farebox account balance and spending totals in USD. |
| get_usageC | Get recent usage statistics from your Farebox account. |
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
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.
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.
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.
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.