Borrowing it
Nothing to install: this file belongs to mrslbt/tabedata-mcp. 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/mrslbt/tabedata-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/mrslbt/tabedata-mcpWrote 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/mrslbt/tabedata-mcp/agents-md)<a href="https://agentmods.dev/instructions/mrslbt/tabedata-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/mrslbt/tabedata-mcp/agents-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/mrslbt/tabedata-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/mrslbt/tabedata-mcp/agents-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 | $0.03449 | $0.03449 |
| Opus 5.5 | $0.01380 | $0.01380 |
| Sonnet 5.5 | $0.00690 | $0.00690 |
| Haiku 4.5 | $0.00345 | $0.00345 |
Grade A, and why
tabedata-mcp AGENTS.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 1mo 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — tabedata-mcp
Instructions for AI coding agents (Codex, Cursor, Copilot, Claude Code, etc.) contributing to this repository.
What this project is
tabedata-mcp is a Model Context Protocol server for Japanese food nutrition data. The dataset is AI-compiled: each value was gathered by an AI assistant (Claude) reading the cited source — a manufacturer label, a restaurant nutrition PDF, Japan's MEXT Standard Tables of Food Composition, or a flagged estimate — then schema-checked. It is not individually human-verified; treat values as a best-effort reference and confirm against source.url. ~10% (46 of 460) are estimates marked source.type: "estimated", all at confidence: "medium". The JSON files in src/data/ are the single source of truth, inlined into the bundle at build time; there is no live data fetch at runtime.
6 tools, 460 items as of v1.2.0:
| Tool | What it does |
|---|---|
search_food |
Bilingual fuzzy search across the curated DB. |
konbini_item |
Chain-scoped lookup for 7-Eleven, Lawson, FamilyMart, Ministop. |
restaurant_meal |
Chain meal lookup with size variants (並 / 大盛 / 特盛). |
analyze_meal |
Natural-language meal analyzer with macro totals + target comparison. |
find_alternatives |
Swap an item for a better one on a chosen axis (protein, calorie, sodium). |
daily_targets |
Mifflin-St Jeor BMR × activity × goal-driven deficit/surplus. |
Distribution: npm (tabedata-mcp), Glama (glama.json configured), Official MCP Registry (server.json).
Quick commands
npm install # install dependencies (Node >= 20)
npm run typecheck # tsc --noEmit — must pass before commit
npm run build # tsup → dist/index.js (ESM, node20 target)
npm test # full integration test (skips live MCP if SKIP_LIVE=1)
npm run test:unit # node --test test/unit/*.test.mjs — 49 unit tests
npm run test:live # full integration including live MCP roundtrip
npm run dev # tsx watch via stdio
npm run dev:http # tsx watch via Streamable HTTP on :8787
npm run verify-data # CI gate — required fields, id uniqueness, URL liveness
npm run stats # coverage progress report
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.
- 1mo ago First seen · 243 lines · 3,449 tokens per session scan A 505e82763a6e
tabedata-mcp AGENTS.md is an instructions file published in the GitHub repository mrslbt/tabedata-mcp (2 stars, last pushed 2mo ago), licensed MIT. It adds 3,449 tokens to every session, about $0.0138 per session on Opus 5.5. 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.