mcp

Query usage statistics from Splitrail's MCP server.

216|23|Updated Jul 12, 2025
One-click install
npx skills add https://github.com/Piebald-AI/splitrail --skill mcp-piebald-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mcp
Source: https://github.com/Piebald-AI/splitrail/tree/main/.claude/skills/mcp
Command: npx skills add https://github.com/Piebald-AI/splitrail --skill mcp-piebald-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Splitrail's MCP server enables developers to extend and customize how AI assistants access and manage usage tooling and resources.

Core Features & Use Cases

  • Tool addition and management: Add new tools and resources to the MCP server to empower AI agents.
  • Interface customization: Modify the MCP interface to fit workflow needs.
  • Use cases: Integrate tool catalogs, query usage data, and automate tooling workflows.

Quick Start

Start the MCP server by running cargo run -- mcp and then register tools using the built-in tool registry.

Frequently Asked Questions about mcp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add new tools to an MCP server for AI assistants?

To add new tools to an MCP server, you register them using the built-in tool registry after starting the server. This enables AI agents to access customized resources and manage usage tooling.

How do I query usage statistics from Splitrail using an MCP server?

You query usage statistics by exposing endpoints like get_daily_stats and get_model_usage. This requires a Rust-based MCP server with defined tool handlers in the server module.

Can I customize the MCP interface to fit my coding workflow?

Yes, you can modify the MCP interface to fit your workflow needs. The server allows tooling teams to integrate tool catalogs, query usage data, and automate tooling workflows across multiple environments.

Do I need Rust to run the Splitrail MCP server?

Yes, you need Rust to run the Splitrail MCP server. You start it by running cargo run -- mcp, and it requires defined tool handlers and data types in the src/mcp directory.

What are the limitations of modifying MCP server interfaces for AI agents?

Modifying MCP server interfaces requires maintaining Rust-based tool handlers and data types in specific source files. Interface customization depends on correctly defining optional resource URIs to expose endpoints.

What is the best way to integrate new analytics across multiple coding tools?

The best way to integrate analytics across coding tools is adding resources and modifying interfaces via an MCP server. This empowers AI agents to automate tooling workflows and query usage data.