Borrowing it
Nothing to install: this file belongs to ginomoretta-creator/gmat-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ginomoretta-creator/gmat-mcp-server/main/CLAUDE.mdgit clone --depth 1 https://github.com/ginomoretta-creator/gmat-mcp-serverWrote 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/instructions/ginomoretta-creator/gmat-mcp-server/claude-md)<a href="https://agentmods.dev/instructions/ginomoretta-creator/gmat-mcp-server/claude-md"><img src="https://agentmods.dev/badge/instructions/ginomoretta-creator/gmat-mcp-server/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/ginomoretta-creator/gmat-mcp-server/claude-md"><img src="https://agentmods.dev/badge/instructions/ginomoretta-creator/gmat-mcp-server/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.01004 | $0.01004 |
| Opus 5 | $0.00502 | $0.00502 |
| Sonnet 5 | $0.00201 | $0.00201 |
| Haiku 4.5 | $0.00100 | $0.00100 |
Grade A, and why
gmat-mcp-server CLAUDE.md 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
This is a GMAT Documentation MCP (Model Context Protocol) Server that provides semantic search capabilities over GMAT documentation. The server scrapes, parses, chunks, and embeds GMAT documentation content to enable AI assistants to access comprehensive GMAT information through vector search.
Architecture
The project follows a modular architecture with the following phases:
Phase 1: Setup and Dependencies
- TypeScript-based Node.js project using
@modelcontextprotocol/sdkandzod - Uses pnpm as package manager (version 10.10.0)
- Core dependencies for scraping (axios, cheerio), embeddings (openai), and vector search
Phase 2: Data Ingestion Pipeline
- Scraping:
src/utils/scraper.ts- Fetches HTML from GMAT documentation site - Parsing:
src/utils/parser.ts- Converts HTML to structured chunks using Cheerio, chunks by headings - Embedding:
src/utils/embedder.ts- Generates embeddings using OpenAI's text-embedding-3-small model - Caching:
src/utils/cache.ts- Persists embedded chunks to filesystem for performance
Phase 3: MCP Server
- Search Engine:
src/utils/search.ts- In-memory cosine similarity search over embeddings - Local Embedder:
src/utils/localEmbedder.ts- Query/corpus embeddings via transformers.js (all-MiniLM-L6-v2, 384d); no API key needed at runtime.src/reembedLocal.tsmigrates an OpenAI-embedded corpus to local embeddings (npm run reembed). - GMAT Tools:
src/utils/gmat.ts- runGmat (async headless execution + outcome classification), getGmatIdioms, listGmatSamples, getGmatSample - Server:
src/index.ts- Main MCP server that loads cached data and handles protocol requests
Common Commands
# Setup (install dependencies)
pnpm install
# Build TypeScript
pnpm run build # Will need to add to package.json
# Development mode
pnpm run dev # Will need to add to package.json
# One-time data ingestion (run before starting server)
pnpm run setup # Will need to add to package.json
# Start MCP server
pnpm start # Will need to add to package.json
# Tests
pnpm test # Will need to add to package.json
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.
- 9d ago First seen · 91 lines · 1,004 tokens per session scan A ec712aa3fc1f
gmat-mcp-server CLAUDE.md is an instructions file published in the GitHub repository ginomoretta-creator/gmat-mcp-server (2 stars, last pushed 3mo ago), licensed ISC. It adds 1,004 tokens to every session, about $0.0050 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 2026-08-31.
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