Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/piebald-ai/splitrail/mcpnpx skills add Piebald-AI/splitrail --skill mcpgit clone --depth 1 https://github.com/Piebald-AI/splitrailWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/piebald-ai/splitrail/mcp)<a href="https://agentmods.dev/skills/piebald-ai/splitrail/mcp"><img src="https://agentmods.dev/badge/skills/piebald-ai/splitrail/mcp.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00026 | $0.00333 |
| Opus 5 | $0.00013 | $0.00167 |
| Sonnet 5 | $0.00005 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00033 |
Grade A, and why
mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
MCP Server
Splitrail can run as an MCP server, allowing AI assistants to query usage statistics programmatically.
cargo run -- mcp
Source Files
src/mcp/mod.rs- Module exportssrc/mcp/server.rs- Server implementation and tool handlerssrc/mcp/types.rs- Request/response types
Available Tools
get_daily_stats- Query usage statistics with date filteringget_model_usage- Analyze model usage distributionget_cost_breakdown- Get cost breakdown over a date rangeget_file_operations- Get file operation statisticscompare_tools- Compare usage across different AI coding toolslist_analyzers- List available analyzers
Resources
splitrail://summary- Daily summaries across all datessplitrail://models- Model usage breakdown
Adding a New Tool
- Define the tool handler in
src/mcp/server.rsusing the#[tool]macro - Add request/response types to
src/mcp/types.rsif needed
See existing tools in src/mcp/server.rs for the pattern.
Adding a New Resource
- Add URI constant to
resource_urismodule insrc/mcp/server.rs - Add to
list_resources()method - Handle in
read_resource()method
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 46 lines · 26 tokens per session scan A eb12c6848dce
mcp is a skill published in the GitHub repository Piebald-AI/splitrail (218 stars, last pushed 13d ago), licensed MIT. It adds 26 tokens to every session and 333 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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